>>Hey guys, and gals. Welcome back to Las Vegas. Lisa Martin here at Coupa inspire 2022 with about 2,500 folks. Very excited to be back in person. I can assure you that is the vibe that is here to be. You joins me next to the managing director at KPMG Toby. It's great to have you on the program. >>Thanks. It's great to be here. >>Isn't it great to be back? I know it feels so normal. We were talking before we went live, that it feels normal. >>It does. It does. And it feels great. And after a great kickoff with, uh, with Rob >>Fantastic, Rob Bernstein has, and Barbara Corcoran, Rob has probably the highest energy of a CEO that I've ever gotten to work with. So you always know you're in for a good high energy conversation. Then Barbara Corcoran coming in, Jon Taffer with bar rescue is it's a, been a great morning so far. So you let's talk about you, you specialize in digital transformation within the procurement and the contract management spaces. Talk to me a little bit about that. >>Yeah, absolutely. You know, I, uh, I love helping folks to re-imagine their, uh, operating models to solve today's challenges. And there are so many challenges coming out in this post COVID world, um, that many of our clients are dealing with. And, and I'm never short on phone calls and, you know, uh, from, from my clients reaching out for help, um, to really figure out how to retool, um, and, and, and really help themselves to transform, to be able to address the, the, the changes to come. >>I heard a really smart description of the last two years today, compressed transformation. We've been talking about digital transformation for years, and then we've also been talking about it's acceleration during the COVID era, but the compressed transformation, I thought that's probably something that's here to stay. Nobody's going to want access to older, less data slower. >>Yep. >>They're just not >>A hundred percent. What >>Are some of the trends that you've observed in your role in the last couple of years? >>Yeah, I, I absolutely believe that folks that took advantage of that digital transformation pre pandemic have actually been able to fare much better than those that have held off on those investments. Um, for whatever reasons, you know, there are always different priorities, but those that have actually gotten that journey started, um, pre pandemic have definitely fared, uh, for, well, I think the trends that I'm seeing today, the CPO's challenge, um, and there are many challenges, um, but you know, the, you know, coming out of the, uh, post COVID era, you are now recovering and ramping up production as a result, your buying activities increasing, right. Um, and, and other ways other than increasing, um, activity. There's also the changing of requirements. So, you know, the folks in the front office are looking at new technologies to innovate new products and services, and that's going to change what the, the mix of the skills and resources that you need in the back office. >>Um, in addition to that, um, there are other requirements like ESG. And so as you're thinking about retooling and being able to, um, buy more sustainably or drive diversity, um, with the spend that you have, that's also changing the skill mix that you have. And I think on top of speak, uh, on top of that, um, the skills and the talent, we are dealing with the, a unfortunate situation that many companies are with the, uh, you know, the great resignation where the talent is, has as quickly exited the workforce. Um, and, uh, and, and with the demand increasing and changing, that puts everyone in a tough spot. And so those are really the big challenges that I've seen with the clients. Most recently, as we're coming out of COVID >>Of your customer conversations, escalated up the C-suite you talk, you mentioned the chief procurement officer. If we think of every company, these days has to be a data company to be successful. If they're not, they're probably not going to be around. Are you noticing that from a supply chain perspective within procurement and contract management, is that escalating the C-suite to be much more of a C-suite or board level initiative? >>Absolutely. Absolutely. I think what folks have realized in many of their, even the earlier digital transformation efforts, it was very geared around automating and streamlining transactions and processes, not so much putting data at the core. Yes, you would get intelligence out of that, but we hadn't architected your entire organization around data and good quality data and what is needed, um, to be able to actually translate that data to meaningful insights, to make the decisions or drive, um, visibility within to your, into your supply chain. Um, so when you think about things that are, um, such as ESG, where you really need to know, um, your tier one, tier two tier three suppliers, and all the impacts that that has, um, in order to drive to those, um, ESG objectives that you're telling your investors, you're telling your customers, and you're telling your, um, your employees about it's very important. You have to be centered around data and be able to be able to see their entire supply chain. And if you weren't, if you weren't architected to do so, doing it as an afterthought is very costly because you've already made those investments >>Very costly. And also, I mean, from a business perspective, I think, you know, we, we talk so often Toby and you probably do as well about it, business alignment. It's one of those, it's like digital transformation. It's almost a buzzword if you will, but it's critical because I'm seeing a lot of data and research from, from folks like Gardner that are showing that massive percentages of businesses believe that the technology is really the driver and the fuel of the business going forward. So no longer can it and lines of business be separated. >>Yeah, I, I totally agree. I actually think that when I mentioned about new skills, if you think about the next generation and the new operating models, um, uh, you know, the, the, the new folks coming out of college have to have that skill set because process and technology are, are, are completely linked. Um, and I think that the organizations, the future and the sick, the most successful ones will know how to actually be more human centric and be able to harness the data through the technologies. So I'll actually allow you and I to do what we do best, right, which is collaborate and negotiate deals work on our relationship versus focused on the technology or entering data into forms and all the administrative components that, uh, many of my clients are plagued with today, >>Collaboration, I think has maybe become even more important in the last two years that we've been so limited about how to collaborate. Thankfully, we have a lot of technologies to do that, but when I think of Coupa collaboration, community are two words that jump out. Talk to me a little bit about from an, a partnership perspective alignment there with the collaborative spirit at KPMG. >>Yeah, absolutely. Um, you know, for, for us, uh, I recently just presented on a very similar topic that nothing great in business is done by a single person. And it takes partners to be able to drive the innovation needed to solve the new challenges of tomorrow. And, and I see our relationship with that. You know, they offer a platform, they offer a method to get access to the data and simplify it in a way for our clients so that they can focus on the relationships and driving the collaboration with their suppliers. And, and I think that that's, that's the thought leadership, uh, in partnership with, uh, with them that we'd like to bring to the table. >>Speaking of alignment between KPMG and Qubit. Talk to me a little bit about ESG as, as sort of a new initiative within KPMG. Talk to me a little bit about that. And what's some of the high level objectives are >>Absolutely. Um, I wouldn't say that it's, it's, it's new. I think it's always been there and there's always been a focus, but I think the recent events and with the regulatory environment changing as well, and as with consumers, consumer behavior, driving and investor community driving towards, um, uh, ESG, I think that is quickly changing how companies are prioritizing that within the Mo amongst everything else that they have. And as a result, I think the CPO's role in that equation is ever so important when it comes to delivering and operationalizing ESG. >>I imagine it, the CPS role must be a lot more strategic these >>Days >>Because they really have to be kind of a transformation change agent. >>Yeah. And actually in most cases, the CPO is perfect for that because that's been their role, um, in, uh, in, in, uh, in many cases before. Um, and I think, yeah, this is just yet another dimension that they didn't have to attack and, and incorporate into the, uh, into the process of selecting the right partner or the right supplier within their, um, within the, uh, with, with who they want to onboard for, for the company. >>Got it. Okay. Let's talk about advice now for companies that are either in the early stages of the supply chain transformation really digitizing, how do they get started? Is it too late for some? >>No, I don't think it's ever too late. I don't think, I, I think, um, I don't think it's too late, you know, and especially with the very big focus on digital and tech these days, sometimes being the late, being late to the game allows folks to actually work out the kinks for, you know, the bleeding edge technologies. And so that makes it even less risky for them to adopt in, in many cases. Um, that's, that's, uh, that, that's what we've seen, but, you know, I think the advice is get educated, uh, really just understand as much as you can around what other people are doing. Are there other, um, uh, peer group, uh, companies like yours, you know, like themselves that are actually going through the transformation or have gone before and just kind of understand what were the drivers of that strategy and what were the outcomes that you can learn from them, get help from externals. >>Um, and whether they be technology partners, consultants, and actually hiring new skills and bringing in new perspectives to help you to own and drive that strategy important. This is super important and you can't outsource these things, right. This needs to come from within, especially when you think about things as purposeful and impactful as ESG. Um, those, those cannot be outsourced. Um, and I think those would be the, uh, the kind of the two key things. Um, but I always also say, um, take an outward in approach, as you're thinking about your new strategy, focus on what your employees are saying about, you know, your supply chain and how easy it is to actually understand and, and work within your supply chain. Talk to your suppliers, talk to your internal business partners, to really reflect and understand how do you make this process as easy as possible for them to comply with. >>I think one of the things I was reading, uh, in preparation for coming here is that some, some survey, a survey that that Cooper did of about 800 decision makers. And one of the things that was overwhelming as a theme is that a lot of organizations don't feel that they have the right data visibility to drive an ESG strategic initiative. So what Coupa does providing that visibility and the ability to collaborate and share across the community is, seems to be something that's going to be a business critical must have going forward. >>Yeah, a hundred percent, you know, many, uh, many of our clients operate under, you know, uh, not under like mandates or compliance, driven, um, kind of policies in the commercial world, many cases you have to influence the buying behavior. And so you can't do that without data. I'd like to think in this day and age presented with the right supplier options with them at the right point in time, you're able to influence and drive the spend to diverse candidates, sustainable options, you know, and there's, you know, not just savings, not just the lowest cost option, but there's so many other things to consider in this day and age. And I think that's where it's so important to be able to have a platform like Hoopa, to be able to gather that data acquire external sources of data, such as ESG related data and make that to, um, to, to all parties, um, and be that source of truth so that you can drive the >>Here's some truth. And also even something that was talked about this morning during the keynote is accountability. And have you heard Jon Taffer from bar rescue talking this morning, but he was talking about an 120 bar rescues. He goes, I've never met one person that has admitted from day one of the four days. They shoot that I'm responsible for the reason that my business is not successful. He goes, everybody has an excuse. There's no accountability until you really force someone to take probably that hard look in the mirror that they don't want to take, but that accountability within organizations within an overall business is critical. >>Yeah, I think, uh, I absolutely believe that went away to solve that is providing the data and making it available. And, um, and really once again, I think it goes back to driving that behavior that you want. And I think it starts with, uh, with, with leadership and I think the accountability, accountability of leadership, and to be able to drive that type of culture within your organization. Um, but absolutely you need data to be able to do that and, and be able to monitor that as well, you know, as a leader to make sure that that accountability is appropriately distributed. >>Right. But one of the things, I mean, I think patients has been in short supply the last two years have been, we've learned that. I think also that another thing we've learned is that access to real-time data is no longer, oh, then that would be great. It's you've got to have that for your business to be differentiated because the, you know, if we think about the consumer side, the consumers are so vocal on things like social media, if the experience isn't tailored, personalized and instantaneous, We have a very short Rob talked about the very short attention span that his kids have. I'm like three minutes. We don't even have that in business or on the consumer side. I don't think. >>Yeah, I, yeah, I see that in my kids and what he said today was, was spot on. Um, so, you know, when I think about my career and where I'm at, and he said the same thing, I mean, our kids are coming into the, there'll be in procurement organizations very soon, sooner than, you know, then, then I like to admit. Um, and as a result, I think that, um, we talked a bit about talent shortage and the challenge with keeping talent. And I think that what you had just expressed is very important is that that experience for the employee, but you come into a workforce and they expect you to have these quick turnarounds, but you've, you offered them tools that require spreadsheets and old archaic systems to be able to solve today's challenges. I think that you're not going to be able to retain your talent right along. Right. >>That's a great point. That's an absolutely fantastic point. Last question for you before we wrap here is so the changes that organizations need to make with respect to being prepared for ESG reporting requirements that are coming down the pike, obviously being, having a data strategy has got to be one of us. >>Yeah, absolutely. I think, um, I think we, many procurement organizations were really geared around savings and a very compliance, driven manner. And when you think about ESG, I think you gotta be very data-driven. Um, and so that should be a priority focus of how do you retool yourself to be able to acquire mass amounts of data, figuring out where you need to go, um, to get that data, whether they be third parties, whether they be directly from the supplier, um, and be able to aggregate it and provide the insight into those reporting standards that are required. Um, and then to be able to actually measure progress along those sustainability or diversity goals that it might be established at, at, at the leadership level. So I think it's coming down the pike. It's a matter of time. I think it's, I think it's, uh, you know, it's something that I've been waiting for to see. Um, and it's interesting to see how, uh, how quickly that it's, it's come down. Um, but I think with the regulatory compliance coming down, um, this is going to be moving very quick and people need to get ready. >>That's good. They need to be ready. Excellent to be thank you for joining me on the program today, talking about what you were doing at KPMG, what it's doing with Kupa and how organizations really should be thinking about and approaching supply chain, digital transformation. We appreciate your insights. >>Yeah, absolutely. Thank you so much. All >>Right. For Toby, you I'm Lisa Martin. You're watching the cube in Las Vegas at Cooper inspire 2022 stick around. My next guest will join me shortly.

>>Welcome back to Los Angeles, Lisa Martin and Dave Nicholson here on day three of the cubes, coverage of coop con and cloud native con north America, 21, Dave, we've had a lot of great conversations. The last three days it's been jam packed. Yes, it has been. And yes, it has been fantastic. And it's been live. Did we mention that it's inline live in Los Angeles and we're very pleased to welcome one of our alumni back to the program. David Stephanie is here. The CEO of Trulio David. Welcome back. It's good to see you. >>Thanks for having me. It's good to be here. Isn't it great to be in person? Oh man. It's been a reunion. >>It hasn't been a reunion and they have Ubered been talking about these great little, have you seen these wristbands that they have? I actually asked >>For two, cause I'm a big hugger, so >>Excellent. So, so here we are day three of coupon. That's actually probably day five, our third day of coverage. I'm losing track to it's Friday. I know that, that I can tell you, you guys announced two dot five a couple of weeks ago. Tell us what's in that. What's exciting. Before we crack open Twilio, uh, choy. >>Sure, sure. Well, it's been exciting to be here. Look, the theme right of resiliency realize has been it's right up our wheelhouse, right? To signal that more people are getting into production type of environments. More people require data protection for cloud native applications, right? And, uh, there's two dot five releases. It is as an answer to what we're seeing in the market. It really is centered predominantly around, uh, ransomware protection. And uh, you know, for us, when we look at this, I I've done a lot of work in, in cybersecurity, my career. And we took a hard look about a year ago around this area. How do we do this? How do we participate? How do we protect and help people recover? Because recovery that's part of the security conversation. You can talk about all the other things, but recovery is just as important. And we look at, uh, everything from a zero trust architecture that we provide now to adhering, to NIST standards and framework that's everything from immutability. Uh, so you can't touch the backups now, right? Uh, th that's fine to encryption, right? We'll encrypt from the application all the way to that, to the storage repository. And we'll leverage Keem in that system. So it's kind of like Bitcoin, right? You need a key to get your coin. You as an end-user only have your key to your data alone. And that's it. So all these things become more and more important as we adopt more cloud native technology. And >>As the threat landscape changes dramatically. >>Oh yeah. I got to tell you right. Every time we, you, you publish an application into another cloud, it's a new vector, right? So now I'm living in a multi-cloud world where multiple applications in my data now lives, right? So people are trying to attack backups through, uh, consoles and the ministry of consoles to the actual back of themselves. So new vectors, new problems need new solutions. >>And you mentioned, you mentioned something, you, you, you asked the question, how do we participate? And we are here at KU con uh, w uh, cloud native foundation. So what about, what's your connection to the open source community and efforts there? How do you participate in that? >>Yeah, so it's a really great question because, you know, uh, we are a closed source solution that focuses all of our efforts on the open source community and protecting cloud native applications. Our roots have been protecting cloud native applications since 2013, 2014, and with a lot of very large logos. And, um, you know, through time there are open source projects that do emerge, you know, in this community. And for example, Valero is an open source data protection platform, um, for all of its goodness, as a, as a community-based project, they're also deficiencies, right? So Valero in itself is, uh, focuses only on label based applications. It doesn't really scale. It doesn't have a UI it's really CLI driven, which is good for some people and it's free. But you know, if you need to really talk about an enterprise grade platform, this is where we pick up, you know, we, in our last release, we gave you the ability to capture your Valero based backups. And now you want to be an adult with an enterprise caliber, you know, backup solution and continue to protect your environment and have compliance and governance needs all satisfied. That's where, that's where we really stand out. >>Well, when you're talking to customers in any industry, what are the things that you talk about in terms of relief, categorizing the key differentiators that really make Trulia stand out above the competition? >>Yeah. Cause there, there a bunch of, they're a bunch of great competitors out there. There's no doubt about it. A lot of the legacy folks that you do see perhaps on those show floor, they do tuck in Valero and under the, under the covers, they can check a box or you can set aside some customer needs some of the pure play people that, that we do see out there, great solutions too. But really where we shine is, you know, we are the most flexible agnostic solution that there is in this market. And we've had people like red hat and Susa and verandas, digital ocean and HPS morale. And the list goes on, certify, say, Trulio is the solution of choice. And now no matter where you are in this journey or who you're using, we have your back. So there's a lot of flexibility. There we are complete storage agnostic. >>We are cloud agnostic in going back to how you want to build our architecture application. People are in various phases in their, in their journey. A lot of times, many moons ago, you may have started with just a label based application. Then you have another department that has a new technique and they want to use helm, or you may be adopting open shift and you're using operators to us. It doesn't matter. You have peace of mind. So whether you have, you have to protect multiple departments or you as an end user, as one single tenant are using various techniques, we'll discover or protect and we can move forward. >>So if you looked at, if you look at it from a workload basis, um, and you look at your customers are the workloads that you're protecting. What's, what's the mix of what you think of as legacy virtualized things versus containerized things. And then, and then, and then the other kind of follow on to that is, um, are you seeing a lot of modernization and migration or are you seeing people leave the legacy things alone and then develop net new in sort of separate silos? >>Yeah. So that's a great question. And I, to tell you the answer varies, that's, that's the honest answer, right? You end up having, you may have a group or a CIO that says, look, your CTO says, we're moving to this new architecture. The water's great, bring your applications in. And so either it's, we're going to lift and shift an application and then start to break it apart over time and develop microservices, or we're gonna start net new. And it really does run, run the gambit. And so, you know, as we look at, for some of those people, they have peace of mind that they can bring their two on applications in and we can recover. And for some people that say, look, I'm going to start brand new, and these are gonna be stateless applications. Um, we've seen this story before, right? Our, our, uh, uh, I joke around, it's kinda like the movie Groundhog's day. >>Uh, you know, we, we started many moons ago within the OpenStack world and we started with stateless to stateful. Always, always, always finds a way, but for the stateless people, um, when you start thinking about security, I've had conversations with CSOs around the world who say, I'm going to publish a stainless application. What I'm concerned about things like drift, you know, what's happening in runtime may be completely different than what I intended. So now we give you the ability to capture that runtime state compare. The two things identify what's changed. If you don't like what you see, and you can take that point in time recovery into a sandbox and forensically take it apart. You know, one of our superpowers, if you will, is the, our point in time, backups are all in an open format. Everyone else has proprietary Schemos. So the benefit of an open format is you have the ability to leverage a lot of third party tooling. So take a point in time, run scanners across it. And it, God forbid Trulio goes away. You still have access and you can recreate a point in time. So when you start thinking about compliance, heavy environments, think about telcos, right? Or financial institutions. They have to keep things for 15 years, right? Technologies change, architectures change. You can't have that lock-in >>So we continue to thrive. And on that front, one of the marketing terms that we hear a lot, and I want to get your opinion on this as a feature proofing, how do you, what does, what does it mean to you and Trillium and how do you enable that for organizations, like you said, for the FSI is I have to keep data for 15 years and other industries that have to keep it for maybe even longer. >>I mean, right. The future proof, uh, you know, terminology, that's part of our mantra actually, when I talked about, you know, a superpower being as agnostic and flexible as can be right, as long as you adhere to standards, right? The standards that are out here, we have that agnostic play. And then again, not just capturing an applications, metadata data, but that open format, right? Giving you that open capability to unpack something. So you're not, there is no, there is no vendor lock-in with us at all. So all these things play a part into, into future-proofing yourself. And because we live and breathe cloud native applications, you know, it's not just Kubernetes right? Over the course of time, there'll be other things, right. You're going to see mixed workloads too. They're gonna be VM based in the cloud and container based in the cloud and server lists as well. But you, as long as you have that framework to continuously build off of it, that's, that's where we go. You know, uh, it shouldn't matter where your application lives, right? At the end of the day, we will protect the application and its data. It can live anywhere. So conversations around multi-cloud change, we start to think and talk across cloud, right? The ability to move your application, your data, wherever it, wherever it needs to be to. >>Well, you talked about recoverability and that is the whole point of backing up video. You have to be able to recover something that we've seen in the last 18, 19 months. Anyone can backup >>Data. >>That's right. That's right. If you can't recover it, or if you can't recover it in time. Yeah. We're talking like going on a business potential and we've seen the massive changes in the security landscape in the last 18, 19 months ransomware. I was looking at some, some cybersecurity data that showed that just in the first half of this calendar year, January one to June 30, 20, 21, ransomware was up nearly 11 X DDoS attacks are up. We've got this remote workforce. That's going to probably persist for a while. So the ability to recover data from not if we get hit by ransomware, but when we get hit by ransomware is >>When you're, you're absolutely right. And, and, and to your plate anyway. So anyone can back up anything. When you look at it, it's at its highest form. We talk about point time where you orchestration, right. Backup is a use case. Dr. Is a use case, right? How do you, reorchestrate something that's complex, right? The containers, these applications in the cloud native space, there are morphous, they're living things, right? The metadata is different from one day to the next, the data itself is different from when one day the net to the next. So that's, what's so great about Trillium. It's such an elegant solution. It allows your, reorchestrate a point in time when and where you need it. So yes. You have to be able to recover. Yes. It's not a matter of if, but when. Right. And that's why recovery is part of that security conversation. Um, you know, I I've seen insurance companies, right? They want to provide insurance for ransomware. Well, you're gonna have enough attacks where they don't want to provide that insurance anymore. It costs too much. The investment that you make with, with Trulio will save you so much more money down the road. Right. Uh, who's our product manager actually gave a talk about that yesterday and the economics were really interesting. >>Hmm. So how has the recovery methodology who participates in that changed over time? As, as we, you know, as we are in this world of developer operators who take on greater responsibility for infrastructure things. Yeah. Who's, who's responsible for backup and recovery today and how, how has that changed >>Everyone? Everyone's responsible. So, you know, we rewind however many years, right? And it used predominantly CIS admin that was in charge of backup administrator, but a ticket in your backup administrator, right. Cloud native space and application lifecycle management is a team sport. Security is a team sport. It's a holistic approach. Right? So when you think about the, the team that you put out on the field, whether your DevOps, your SRE dev sec ops it ops, you're all going to have a need for point in time, we orchestration for various things and the term may not be backup. Right? It's something else. And maybe for test dev purposes, maybe for forensic purposes, maybe for Dr. Right. So I say it's a team sport and security as a holistic thing that everyone has to get on board with >>The three orchestration is exactly the right way to talk about absolute these processes. It's not just recovery, you're rebuilding >>Yeah. A complex environment. It's always changing. >>That's one of the guarantees. It's always going to be changing >>That much. >>Can you give us a, leave us with a customer example that you think really articulates the value of what Trulio delivers? >>Yeah. So it's interesting. I won't say who the customer is, but I'll tell you it's in the defense agency, it's a defense agency. Uh, they have developers all over the place. Uh, they need self-service capabilities for the tenants to mind their own backups. So you don't need to contact someone, right. They can build, they have one >>Dashboard, single pane of glass or truth to manage all their Corinthians applications. And it gives them that infrastructure to progress whether your dev ops or not your it ops, uh, this, this group has rolled it out across the nation and they're using in their work with very sensitive environments. So now we have they're back. And what are some of the big business outcomes that they're achieving already? >>The big business outcomes? Well, so operational efficiencies are definitely first and foremost, right? Empowering the end user with more tools, right? Because we've seen this shift left and people talking about dev ops, right. So how do I empower them to do more? So I see that operational efficiency, the recoverability aspect, God forbid, something goes wrong. How do you, how do you do that in the cost of that? Um, and then also, um, being native to the environment, the Trillium solution is built for Kubernetes. It is built on go. It is a Qubit stateless Kubernetes application. So you have to have seamless integration into these environments. And then going back to what I was saying before, knowing peace of mind, the credibility aspect, that it is blessed by, you know, red hat and suicide Mirandas and all these other, other folks in the field, um, that you can guarantee it's going to work >>Well, that helps to give your customers the confidence that there, and that confidence might sound trivial. It's not, especially when we're talking about security, it's not at all that, that's a, that's a big business outcome for you guys. When a customer says, I'm confident I have the right solution, we're going to be able to recover when things happen, we try, we fully trust in the solution that we're, >>And we'll bring more into production faster that helps everyone out here too. Right? It feels good. You have that credibility. You have that assurance that I can move faster and I can move into different clouds faster. And that's, we're gonna continue to put, we're gonna continue to push the envelope there. You know, coming a, as we look into, you know, going forward, we're going to come out with other capabilities. That's going to continue to differentiate ourselves from, from folks. Uh, we'll, we'll talk about in time, the ability to propagate data across multiple clouds simultaneously. So making RTOs look at the split seconds and minutes. And so I hope that we can have that conversation next time we were together, because it's really exciting. >>Any, any CTA that you want to give to the audience, any, any, uh, like upcoming or recent webinars that you think they would be really benefit from? >>I guess one thing I put out there is that, um, I understand that people need to continuously learn. There is a skillset hole in, in this market. We can, we understand that, you know, and people look to us as not just a vendor, but a partner. And a lot of the questions that we do get are how do I do this? Or how do I do that? Engage us, ask us to consume our product is really, really easy. You can download from the website or go to an, you know, red hats operator hub, or go to the marketplace over at Susa, and let's begin to begin and we're here to help. And so reach out, right? We want everyone to be successful. >>Awesome. trillium.io. David, thank you for joining us. This has been an exciting conversation. Good >>To see you all. >>Likewise. Good to see you in person take care. We look forward to the next time we see you when unpacking what other great things are going on on Trulia. We appreciate your >>Time. Thank you so much. Good to be here >>For David's fie and David Nicholson, the two Davids I'm going to sandwich. I'm Lisa Martin, you we're coming to you live from Los Angeles. This is Q con cloud native con north America, 2021. Stick around our next guest joins us momentarily.

>> Narrator: From around the globe, it's the CUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021, the virtual edition. This is the CUBEs, continuous, deep dive coverage of the people, processes and technologies that are really changing our world. Right now, we're going to talk about modernization and what's beyond with Jamie Thomas, general manager, strategy and development, IBM Enterprise Security. Jamie, always a pleasure. Great to see you again. Thanks for coming on. >> It's great to see you, Dave. And thanks for having me on the CUBE is always a pleasure. >> Yeah, it is our pleasure. And listen, we've been hearing a lot about IBM is focused on hybrid cloud, Arvind Krishna says we must win the architectural battle for hybrid cloud. I love that. We've been hearing a lot about AI. And I wonder if you could talk about IBM Systems and how it plays into that strategy? >> Sure, well, it's a great time to have this discussion Dave. As you all know, IBM Systems Technology is used widely around the world, by many, many 1000s of clients in the context of our IBM System Z, our power systems and storage. And what we have seen is really an uptake of monetization around those workloads, if you will, driven by hybrid cloud, the hybrid cloud agenda, as well as an uptake of Red Hat OpenShift, as a vehicle for this modernization. So it's pretty exciting stuff, what we see as many clients taking advantage of OpenShift on Linux, to really modernize these environments, and then stay close, if you will, to that systems of record database and the transactions associated with it. So they're seeing a definite performance advantage to taking advantage of OpenShift. And it's really fascinating to see the things that they're doing. So if you look at financial services, for instance, there's a lot of focus on risk analytics. So things like fraud, anti money laundering, mortgage risk, types of applications being done in this context, when you look at our retail industry clients, you see also a lot of customer centricity solutions, if you will, being deployed on OpenShift. And once again, having Linux close to those traditional LPARs of AIX, I-Series, or in the context of z/OS. So those are some of the things we see happening. And it's quite real. >> Now, you didn't mention power, but I want to come back and ask you about power. Because a few weeks ago, we were prompted to dig in a little bit with the when Arvind was on with Pat Kessinger at Intel and talking about the relationship you guys have. And so we dug in a little bit, we thought originally, we said, oh, it's about quantum. But we dug in. And we realized that the POWER10 is actually the best out there and the highest performance in terms of disaggregating memory. And we see that as a future architecture for systems and actually really quite excited about it about the potential that brings not only to build beyond system on a chip and system on a package, but to start doing interesting things at the Edge. You know, what do you what's going on with power? >> Well, of course, when I talked about OpenShift, we're doing OpenShift on power Linux, as well as Z Linux, but you're exactly right in the context for a POWER10 processor. We couldn't be more we're so excited about this processor. First of all, it's our first delivery with our partner Samsung with a seven nanometer form factor. The processor itself has only 18 billion transistors. So it's got a few transistors there. But one of the cool inventions, if you will, that we have created is this expansive memory region as part of this design point, which we call memory inception, it gives us the ability to reach memory across servers, up to two petabytes of memory. Aside from that, this processor has generational improvements and core and thread performance, improved energy efficiency. And all of this, Dave is going to give us a lot of opportunity with new workloads, particularly around artificial intelligence and inferencing around artificial intelligence. I mean, that's going to be that's another critical innovation that we see here in this POWER10 processor. >> Yeah, processor performance is just exploding. We're blowing away the historical norms. I think many people don't realize that. Let's talk about some of the key announcements that you've made in quantum last time we spoke on the qubit for last year, I think we did a deeper dive on quantum. You've made some announcements around hardware and software roadmaps. Give us the update on quantum please. >> Well, there is so much that has happened since we last spoke on the quantum landscape. And the key thing that we focused on in the last six months is really an articulation of our roadmaps, so the roadmap around hardware, the roadmap around software, and we've also done quite a bit of ecosystem development. So in terms of the roadmap around hardware, we put ourselves out there we've said we were going to get to over 1000 qubit machine and in 2023, so that's our milestone. And we've got a number of steps we've outlined along that way, of course, we have to make progress, frankly, every six months in terms of innovating around the processor, the electronics and the fridge associated with these machines. So lots of exciting innovation across the board. We've also published a software roadmap, where we're articulating how we improve a circuit execution speeds. So we hope, our plan to show shortly a 100 times improvement in circuit execution speeds. And as we go forward in the future, we're modifying our Qiskit programming model to not only allow a easily easy use by all types of developers, but to improve the fidelity of the entire machine, if you will. So all of our innovations go hand in hand, our hardware roadmap, our software roadmap, are all very critical in driving the technical outcomes that we think are so important for quantum to become a reality. We've deployed, I would say, in our quantum cloud over, you know, over 20 machines over time, we never quite identify the precise number because frankly, as we put up a new generation machine, we often retire when it's older. So we're constantly updating them out there, and every machine that comes on online, and that cloud, in fact, represents a sea change and hardware and a sea change in software. So they're all the latest and greatest that our clients can have access to. >> That's key, the developer angle you got redshift running on quantum yet? >> Okay, I mean, that's a really good question, you know, as part of that software roadmap in terms of the evolution and the speed of that circuit execution is really this interesting marriage between classical processing and quantum processing and bring those closer together. And in the context of our classical operations that are interfacing with that quantum processor, we're taking advantage of OpenShift, running on that classical machine to achieve that. And once again, if, as you can imagine, that'll give us a lot of flexibility in terms of where that classical machine resides and how we continue the evolution the great marriage, I think that's going to that will exist that does exist and will exist between classical computing and quantum computing. >> I'm glad I asked it was kind of tongue in cheek. But that's a key thread to the ecosystem, which is critical to obviously, you know, such a new technology. How are you thinking about the ecosystem evolution? >> Well, the ecosystem here for quantum is infinitely important. We started day one, on this journey with free access to our systems for that reason, because we wanted to create easy entry for anyone that really wanted to participate in this quantum journey. And I can tell you, it really fascinates everyone, from high school students, to college students, to those that are PhDs. But during this journey, we have reached over 300,000 unique users, we have now over 500,000 unique downloads of our Qiskit programming model. But to really achieve that is his back plane by this ongoing educational thrust that we have. So we've created an open source textbook, around Qiskit that allows organizations around the world to take advantage of it from a curriculum perspective. We have over 200 organizations that are using our open source textbook. Last year, when we realized we couldn't do our in person programming camps, which were so exciting around the world, you can imagine doing an in person programming camp and South Africa and Asia and all those things we did in 2019. Well, we had just like you all, we had to go completely virtual, right. And we thought that we would have a few 100 people sign up for our summer school, we had over 4000 people sign up for our summer school. And so one of the things we had to do is really pedal fast to be able to support that many students in this summer school that kind of grew out of our proportions. The neat thing was once again, seeing all the kids and students around the world taking advantage of this and learning about quantum computing. And then I guess that the end of last year, Dave, to really top this off, we did something really fundamentally important. And we set up a quantum center for historically black colleges and universities, with Howard University being the anchor of this quantum center. And we're serving 23 HBCUs now, to be able to reach a new set of students, if you will, with STEM technologies, and most importantly, with quantum. And I find, you know, the neat thing about quantum is is very interdisciplinary. So we have quantum physicist, we have electrical engineers, we have engineers on the team, we have computer scientists, we have people with biology and chemistry and financial services backgrounds. So I'm pretty excited about the reach that we have with quantum into HBCUs and even beyond right I think we can do some we can have some phenomenal results and help a lot of people on this journey to quantum and you know, obviously help ourselves but help these students as well. >> What do you see in people do with quantum and maybe some of the use cases. I mean you mentioned there's sort of a connection to traditional workloads, but obviously some new territory what's exciting out there? >> Well, there's been a really a number of use cases that I think are top of mind right now. So one of the most interesting to me has been one that showed us a few months ago that we talked about in the press actually a few months ago, which is with Exxon Mobil. And they really started looking at logistics in the context of Maritime shipping, using quantum. And if you think of logistics, logistics are really, really complicated. Logistics in the face of a pandemic are even more complicated and logistics when things like the Suez Canal shuts down, are even more complicated. So think about, you know, when the Suez Canal shut down, it's kind of like the equivalent of several major airports around the world shutting down and then you have to reroute all the traffic, and that traffic and maritime shipping is has to be very precise, has to be planned the stops are plan, the routes are plan. And the interest that ExxonMobil has had in this journey is not just more effective logistics, but how do they get natural gas shipped around the world more effectively, because their goal is to bring energy to organizations into countries while reducing CO2 emissions. So they have a very grand vision that they're trying to accomplish. And this logistics operation is just one of many, then we can think of logistics, though being a being applicable to anyone that has a supply chain. So to other shipping organizations, not just Maritime shipping. And a lot of the optimization logic that we're learning from that set of work also applies to financial services. So if we look at optimization, around portfolio pricing, and everything, a lot of the similar characteristics will also go be applicable to the financial services industry. So that's one big example. And I guess our latest partnership that we announced with some fanfare, about two weeks ago, was with the Cleveland Clinic, and we're doing a special discovery acceleration activity with the Cleveland Clinic, which starts prominently with artificial intelligence, looking at chemistry and genomics, and improve speed around machine learning for all of the the critical healthcare operations that the Cleveland Clinic has embarked on but as part of that journey, they like many clients are evolving from artificial intelligence, and then learning how they can apply quantum as an accelerator in the future. And so they also indicated that they will buy the first commercial on premise quantum computer for their operations and place that in Ohio, in the the the years to come. So it's a pretty exciting relationship. These relationships show the power of the combination, once again, of classical computing, using that intelligently to solve very difficult problems. And then taking advantage of quantum for what it can uniquely do in a lot of these use cases. >> That's great description, because it is a strong connection to things that we do today. It's just going to do them better, but then it's going to open up a whole new set of opportunities. Everybody wants to know, when, you know, it's all over the place. Because some people say, oh, not for decades, other people say I think it's going to be sooner than you think. What are you guys saying about timeframe? >> We're certainly determined to make it sooner than later. Our roadmaps if you note go through 2023. And we think the 2023 is going to will be a pivotal year for us in terms of delivery around those roadmaps. But it's these kind of use cases and this intense working with these clients, 'cause when they work with us, they're giving us feedback on everything that we've done, how does this programming model really help me solve these problems? What do we need to do differently? In the case of Exxon Mobil, they've given us a lot of really great feedback on how we can better fine tune all elements of the system to improve that system. It's really allowed us to chart a course for how we think about the programming model in particular in the context of users. Just last week, in fact, we announced some new machine learning applications, which these applications are really to allow artificial intelligence users and programmers to get take advantage of quantum without being a quantum physicist or expert, right. So it's really an encapsulation of a composable elements so that they can start to use, using an interface allows them to access through PyTorch into the quantum computer, take advantage of some of the things we're doing around neural networks and things like that, once again, without having to be experts in quantum. So I think those are the kind of things we're learning how to do better, fundamentally through this co-creation and development with our quantum network. And our quantum network now is over 140 unique organizations and those are commercial, academic, national laboratories and startups that we're working with. >> The picture started become more clear, we're seeing emerging AI applications, a lot of work today in AI is in modeling. Over time, it's going to shift toward inference and real time and practical applications. Everybody talks about Moore's law being dead. Well, in fact, the yes, I guess, technically speaking, but the premise or the outcome of Moore's law is actually accelerating, we're seeing processor performance, quadrupling every two years now, when you include the GPU along with the CPU, the DSPs, the accelerators. And so that's going to take us through this decade, and then then quantum is going to power us, you know, well beyond who can even predict that. It's a very, very exciting time. Jamie, I always love talking to you. Thank you so much for coming back on the CUBE. >> Well, I appreciate the time. And I think you're exactly right, Dave, you know, we talked about POWER10, just for a few minutes there. But one of the things we've done in POWER10, as well as we've embedded AI into every core that processor, so you reduce that latency, we've got a 10 to 20 times improvement over the last generation in terms of artificial intelligence, you think about the evolution of a classical machine like that state of the art, and then combine that with quantum and what we can do in the future, I think is a really exciting time to be in computing. And I really appreciate your time today to have this dialogue with you. >> Yeah, it's always fun and it's of national importance as well. Jamie Thomas, thanks so much. This is Dave Vellante with the CUBE keep it right there our continuous coverage of IBM Think 2021 will be right back. (gentle music) (bright music)

(bright music) >> Narrator: From around the globe, it's the CUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021, the virtual edition. This is the CUBEs, continuous, deep dive coverage of the people, processes and technologies that are really changing our world. Right now, we're going to talk about modernization and what's beyond with Jamie Thomas, general manager, strategy and development, IBM Enterprise Security. Jamie, always a pleasure. Great to see you again. Thanks for coming on. >> It's great to see you, Dave. And thanks for having me on the CUBE is always a pleasure. >> Yeah, it is our pleasure. And listen, we've been hearing a lot about IBM is focused on hybrid cloud, Arvind Krishna says we must win the architectural battle for hybrid cloud. I love that. We've been hearing a lot about AI. And I wonder if you could talk about IBM Systems and how it plays into that strategy? >> Sure, well, it's a great time to have this discussion Dave. As you all know, IBM Systems Technology is used widely around the world, by many, many 1000s of clients in the context of our IBM System Z, our power systems and storage. And what we have seen is really an uptake of monetization around those workloads, if you will, driven by hybrid cloud, the hybrid cloud agenda, as well as an uptake of Red Hat OpenShift, as a vehicle for this modernization. So it's pretty exciting stuff, what we see as many clients taking advantage of OpenShift on Linux, to really modernize these environments, and then stay close, if you will, to that systems of record database and the transactions associated with it. So they're seeing a definite performance advantage to taking advantage of OpenShift. And it's really fascinating to see the things that they're doing. So if you look at financial services, for instance, there's a lot of focus on risk analytics. So things like fraud, anti money laundering, mortgage risk, types of applications being done in this context, when you look at our retail industry clients, you see also a lot of customer centricity solutions, if you will, being deployed on OpenShift. And once again, having Linux close to those traditional LPARs of AIX, I-Series, or in the context of z/OS. So those are some of the things we see happening. And it's quite real. >> Now, you didn't mention power, but I want to come back and ask you about power. Because a few weeks ago, we were prompted to dig in a little bit with the when Arvind was on with Pat Kessinger at Intel and talking about the relationship you guys have. And so we dug in a little bit, we thought originally, we said, oh, it's about quantum. But we dug in. And we realized that the POWER10 is actually the best out there and the highest performance in terms of disaggregating memory. And we see that as a future architecture for systems and actually really quite excited about it about the potential that brings not only to build beyond system on a chip and system on a package, but to start doing interesting things at the Edge. You know, what do you what's going on with power? >> Well, of course, when I talked about OpenShift, we're doing OpenShift on power Linux, as well as Z Linux, but you're exactly right in the context for a POWER10 processor. We couldn't be more we're so excited about this processor. First of all, it's our first delivery with our partner Samsung with a seven nanometer form factor. The processor itself has only 18 billion transistors. So it's got a few transistors there. But one of the cool inventions, if you will, that we have created is this expansive memory region as part of this design point, which we call memory inception, it gives us the ability to reach memory across servers, up to two petabytes of memory. Aside from that, this processor has generational improvements and core and thread performance, improved energy efficiency. And all of this, Dave is going to give us a lot of opportunity with new workloads, particularly around artificial intelligence and inferencing around artificial intelligence. I mean, that's going to be that's another critical innovation that we see here in this POWER10 processor. >> Yeah, processor performance is just exploding. We're blowing away the historical norms. I think many people don't realize that. Let's talk about some of the key announcements that you've made in quantum last time we spoke on the qubit for last year, I think we did a deeper dive on quantum. You've made some announcements around hardware and software roadmaps. Give us the update on quantum please. >> Well, there is so much that has happened since we last spoke on the quantum landscape. And the key thing that we focused on in the last six months is really an articulation of our roadmaps, so the roadmap around hardware, the roadmap around software, and we've also done quite a bit of ecosystem development. So in terms of the roadmap around hardware, we put ourselves out there we've said we were going to get to over 1000 qubit machine and in 2023, so that's our milestone. And we've got a number of steps we've outlined along that way, of course, we have to make progress, frankly, every six months in terms of innovating around the processor, the electronics and the fridge associated with these machines. So lots of exciting innovation across the board. We've also published a software roadmap, where we're articulating how we improve a circuit execution speeds. So we hope, our plan to show shortly a 100 times improvement in circuit execution speeds. And as we go forward in the future, we're modifying our Qiskit programming model to not only allow a easily easy use by all types of developers, but to improve the fidelity of the entire machine, if you will. So all of our innovations go hand in hand, our hardware roadmap, our software roadmap, are all very critical in driving the technical outcomes that we think are so important for quantum to become a reality. We've deployed, I would say, in our quantum cloud over, you know, over 20 machines over time, we never quite identify the precise number because frankly, as we put up a new generation machine, we often retire when it's older. So we're constantly updating them out there, and every machine that comes on online, and that cloud, in fact, represents a sea change and hardware and a sea change in software. So they're all the latest and greatest that our clients can have access to. >> That's key, the developer angle you got redshift running on quantum yet? >> Okay, I mean, that's a really good question, you know, as part of that software roadmap in terms of the evolution and the speed of that circuit execution is really this interesting marriage between classical processing and quantum processing and bring those closer together. And in the context of our classical operations that are interfacing with that quantum processor, we're taking advantage of OpenShift, running on that classical machine to achieve that. And once again, if, as you can imagine, that'll give us a lot of flexibility in terms of where that classical machine resides and how we continue the evolution the great marriage, I think that's going to that will exist that does exist and will exist between classical computing and quantum computing. >> I'm glad I asked it was kind of tongue in cheek. But that's a key thread to the ecosystem, which is critical to obviously, you know, such a new technology. How are you thinking about the ecosystem evolution? >> Well, the ecosystem here for quantum is infinitely important. We started day one, on this journey with free access to our systems for that reason, because we wanted to create easy entry for anyone that really wanted to participate in this quantum journey. And I can tell you, it really fascinates everyone, from high school students, to college students, to those that are PhDs. But during this journey, we have reached over 300,000 unique users, we have now over 500,000 unique downloads of our Qiskit programming model. But to really achieve that is his back plane by this ongoing educational thrust that we have. So we've created an open source textbook, around Qiskit that allows organizations around the world to take advantage of it from a curriculum perspective. We have over 200 organizations that are using our open source textbook. Last year, when we realized we couldn't do our in person programming camps, which were so exciting around the world, you can imagine doing an in person programming camp and South Africa and Asia and all those things we did in 2019. Well, we had just like you all, we had to go completely virtual, right. And we thought that we would have a few 100 people sign up for our summer school, we had over 4000 people sign up for our summer school. And so one of the things we had to do is really pedal fast to be able to support that many students in this summer school that kind of grew out of our proportions. The neat thing was once again, seeing all the kids and students around the world taking advantage of this and learning about quantum computing. And then I guess that the end of last year, Dave, to really top this off, we did something really fundamentally important. And we set up a quantum center for historically black colleges and universities, with Howard University being the anchor of this quantum center. And we're serving 23 HBCUs now, to be able to reach a new set of students, if you will, with STEM technologies, and most importantly, with quantum. And I find, you know, the neat thing about quantum is is very interdisciplinary. So we have quantum physicist, we have electrical engineers, we have engineers on the team, we have computer scientists, we have people with biology and chemistry and financial services backgrounds. So I'm pretty excited about the reach that we have with quantum into HBCUs and even beyond right I think we can do some we can have some phenomenal results and help a lot of people on this journey to quantum and you know, obviously help ourselves but help these students as well. >> What do you see in people do with quantum and maybe some of the use cases. I mean you mentioned there's sort of a connection to traditional workloads, but obviously some new territory what's exciting out there? >> Well, there's been a really a number of use cases that I think are top of mind right now. So one of the most interesting to me has been one that showed us a few months ago that we talked about in the press actually a few months ago, which is with Exxon Mobil. And they really started looking at logistics in the context of Maritime shipping, using quantum. And if you think of logistics, logistics are really, really complicated. Logistics in the face of a pandemic are even more complicated and logistics when things like the Suez Canal shuts down, are even more complicated. So think about, you know, when the Suez Canal shut down, it's kind of like the equivalent of several major airports around the world shutting down and then you have to reroute all the traffic, and that traffic and maritime shipping is has to be very precise, has to be planned the stops are plan, the routes are plan. And the interest that ExxonMobil has had in this journey is not just more effective logistics, but how do they get natural gas shipped around the world more effectively, because their goal is to bring energy to organizations into countries while reducing CO2 emissions. So they have a very grand vision that they're trying to accomplish. And this logistics operation is just one of many, then we can think of logistics, though being a being applicable to anyone that has a supply chain. So to other shipping organizations, not just Maritime shipping. And a lot of the optimization logic that we're learning from that set of work also applies to financial services. So if we look at optimization, around portfolio pricing, and everything, a lot of the similar characteristics will also go be applicable to the financial services industry. So that's one big example. And I guess our latest partnership that we announced with some fanfare, about two weeks ago, was with the Cleveland Clinic, and we're doing a special discovery acceleration activity with the Cleveland Clinic, which starts prominently with artificial intelligence, looking at chemistry and genomics, and improve speed around machine learning for all of the the critical healthcare operations that the Cleveland Clinic has embarked on but as part of that journey, they like many clients are evolving from artificial intelligence, and then learning how they can apply quantum as an accelerator in the future. And so they also indicated that they will buy the first commercial on premise quantum computer for their operations and place that in Ohio, in the the the years to come. So it's a pretty exciting relationship. These relationships show the power of the combination, once again, of classical computing, using that intelligently to solve very difficult problems. And then taking advantage of quantum for what it can uniquely do in a lot of these use cases. >> That's great description, because it is a strong connection to things that we do today. It's just going to do them better, but then it's going to open up a whole new set of opportunities. Everybody wants to know, when, you know, it's all over the place. Because some people say, oh, not for decades, other people say I think it's going to be sooner than you think. What are you guys saying about timeframe? >> We're certainly determined to make it sooner than later. Our roadmaps if you note go through 2023. And we think the 2023 is going to will be a pivotal year for us in terms of delivery around those roadmaps. But it's these kind of use cases and this intense working with these clients, 'cause when they work with us, they're giving us feedback on everything that we've done, how does this programming model really help me solve these problems? What do we need to do differently? In the case of Exxon Mobil, they've given us a lot of really great feedback on how we can better fine tune all elements of the system to improve that system. It's really allowed us to chart a course for how we think about the programming model in particular in the context of users. Just last week, in fact, we announced some new machine learning applications, which these applications are really to allow artificial intelligence users and programmers to get take advantage of quantum without being a quantum physicist or expert, right. So it's really an encapsulation of a composable elements so that they can start to use, using an interface allows them to access through PyTorch into the quantum computer, take advantage of some of the things we're doing around neural networks and things like that, once again, without having to be experts in quantum. So I think those are the kind of things we're learning how to do better, fundamentally through this co-creation and development with our quantum network. And our quantum network now is over 140 unique organizations and those are commercial, academic, national laboratories and startups that we're working with. >> The picture started become more clear, we're seeing emerging AI applications, a lot of work today in AI is in modeling. Over time, it's going to shift toward inference and real time and practical applications. Everybody talks about Moore's law being dead. Well, in fact, the yes, I guess, technically speaking, but the premise or the outcome of Moore's law is actually accelerating, we're seeing processor performance, quadrupling every two years now, when you include the GPU along with the CPU, the DSPs, the accelerators. And so that's going to take us through this decade, and then then quantum is going to power us, you know, well beyond who can even predict that. It's a very, very exciting time. Jamie, I always love talking to you. Thank you so much for coming back on the CUBE. >> Well, I appreciate the time. And I think you're exactly right, Dave, you know, we talked about POWER10, just for a few minutes there. But one of the things we've done in POWER10, as well as we've embedded AI into every core that processor, so you reduce that latency, we've got a 10 to 20 times improvement over the last generation in terms of artificial intelligence, you think about the evolution of a classical machine like that state of the art, and then combine that with quantum and what we can do in the future, I think is a really exciting time to be in computing. And I really appreciate your time today to have this dialogue with you. >> Yeah, it's always fun and it's of national importance as well. Jamie Thomas, thanks so much. This is Dave Vellante with the CUBE keep it right there our continuous coverage of IBM Think 2021 will be right back. (gentle music) (bright music)

>> My name is Franco Nori. Is a great pleasure to be here and I thank you for attending this meeting and I'll be talking about some of the work we are doing within the NTT-PHI group. I would like to thank the organizers for putting together this very interesting event. The topics studied by NTT-PHI are very exciting and I'm glad to be part of this great team. Let me first start with a brief overview of just a few interactions between our team and other groups within NTT-PHI. After this brief overview or these interactions then I'm going to start talking about machine learning and neural networks applied to computationally difficult problems in quantum physics. The first one I would like to raise is the following. Is it possible to have decoherence free interaction between qubits? And the proposed solution was a postdoc and a visitor and myself some years ago was to study decoherence free interaction between giant atoms made of superconducting qubits in the context of waveguide quantum electrodynamics. The theoretical prediction was confirmed by a very nice experiment performed by Will Oliver's group at MIT was probably so a few months ago in nature and it's called waveguide quantum electrodynamics with superconducting artificial giant atoms. And this is the first joint MIT Michigan nature paper during this NTT-PHI grand period. And we're very pleased with this. And I look forward to having additional collaborations like this one also with other NTT-PHI groups, Another collaboration inside NTT-PHI regards the quantum hall effects in a rapidly rotating polarity and condensates. And this work is mainly driven by two people, a Michael Fraser and Yoshihisa Yamamoto. They are the main driving forces of this project and this has been a great fun. We're also interacting inside the NTT-PHI environment with the groups of marandI Caltech, like McMahon Cornell, Oliver MIT, and as I mentioned before, Fraser Yamamoto NTT and others at NTT-PHI are also very welcome to interact with us. NTT-PHI is interested in various topics including how to use neural networks to solve computationally difficult and important problems. Let us now look at one example of using neural networks to study computationally difficult and hard problems. Everything we'll be talking today is mostly working progress to be extended and improve in the future. So the first example I would like to discuss is topological quantum phase transition retrieved through manifold learning, which is a variety of version of machine learning. This work is done in collaboration with Che, Gneiting and Liu all members of the group. preprint is available in the archive. Some groups are studying a quantum enhanced machine learning where machine learning is supposed to be used in actual quantum computers to use exponential speed-up and using quantum error correction we're not working on these kind of things we're doing something different. We're studying how to apply machine learning applied to quantum problems. For example how to identify quantum phases and phase transitions. We shall be talking about right now. How to achieve, how to perform quantum state tomography in a more efficient manner. That's another work of ours which I'll be showing later on. And how to assist the experimental data analysis which is a separate project which we recently published. But I will not discuss today because the experiments can produce massive amounts of data and machine learning can help to understand these huge tsunami of data provided by these experiments. Machine learning can be either supervised or unsupervised. Supervised is requires human labeled data. So we have here the blue dots have a label. The red dots have a different label. And the question is the new data corresponds to either the blue category or the red category. And many of these problems in machine learning they use the example of identifying cats and dogs but this is typical example. However, there are the cases which are also provides with there are no labels. So you're looking at the cluster structure and you need to define a metric, a distance between the different points to be able to correlate them together to create these clusters. And you can manifold learning is ideally suited to look at problems we just did our non-linearities and unsupervised. Once you're using the principle component analysis along this green axis here which are the principal axis here. You can actually identify a simple structure with linear projection when you increase the axis here, you get the red dots in one area, and the blue dots down here. But in general you could get red green, yellow, blue dots in a complicated manner and the correlations are better seen when you do an nonlinear embedding. And in unsupervised learning the colors represent similarities are not labels because there are no prior labels here. So we are interested on using machine learning to identify topological quantum phases. And this requires looking at the actual phases and their boundaries. And you start from a set of Hamiltonians or wave functions. And recall that this is difficult to do because there is no symmetry breaking, there is no local order parameters and in complicated cases you can not compute the topological properties analytically and numerically is very hard. So therefore machine learning is enriching the toolbox for studying topological quantum phase transitions. And before our work, there were quite a few groups looking at supervised machine learning. The shortcomings that you need to have prior knowledge of the system and the data must be labeled for each phase. This is needed in order to train the neural networks . More recently in the past few years, there has been increased push on looking at all supervised and Nonlinear embeddings. One of the shortcomings we have seen is that they all use the Euclidean distance which is a natural way to construct the similarity matrix. But we have proven that it is suboptimal. It is not the optimal way to look at distance. The Chebyshev distances provides better performance. So therefore the difficulty here is how to detect topological quantifies transition is a challenge because there is no local order parameters. Few years ago we thought well, three or so years ago machine learning may provide effective methods for identifying topological Features needed in the past few years. The past two years several groups are moving this direction. And we have shown that one type of machine learning called manifold learning can successfully retrieve topological quantum phase transitions in momentum and real spaces. We have also Shown that if you use the Chebyshev distance between data points are supposed to Euclidean distance, you sharpen the characteristic features of these topological quantum phases in momentum space and the afterwards we do so-called diffusion map, Isometric map can be applied to implement the dimensionality reduction and to learn about these phases and phase transition in an unsupervised manner. So this is a summary of this work on how to characterize and study topological phases. And the example we used is to look at the canonical famous models like the SSH model, the QWZ model, the quenched SSH model. We look at this momentous space and the real space, and we found that the metal works very well in all of these models. And moreover provides a implications and demonstrations for learning also in real space where the topological invariants could be either or known or hard to compute. So it provides insight on both momentum space and real space and its the capability of manifold learning is very good especially when you have the suitable metric in exploring topological quantum phase transition. So this is one area we would like to keep working on topological faces and how to detect them. Of course there are other problems where neural networks can be useful to solve computationally hard and important problems in quantum physics. And one of them is quantum state tomography which is important to evaluate the quality of state production experiments. The problem is quantum state tomography scales really bad. It is impossible to perform it for six and a half 20 qubits. If you have 2000 or more forget it, it's not going to work. So now we're seeing a very important process which is one here tomography which cannot be done because there is a computationally hard bottleneck. So machine learning is designed to efficiently handle big data. So the question we're asking a few years ago is chemistry learning help us to solve this bottleneck which is quantum state tomography. And this is a project called Eigenstate extraction with neural network tomography with a student Melkani , research scientists of the group Clemens Gneiting and I'll be brief in summarizing this now. The specific machine learning paradigm is the standard artificial neural networks. They have been recently shown in the past couple of years to be successful for tomography of pure States. Our approach will be to carry this over to mixed States. And this is done by successively reconstructing the eigenStates or the mixed states. So it is an iterative procedure where you can slowly slowly get into the desired target state. If you wish to see more details, this has been recently published in phys rev A and has been selected as a editor suggestion. I mean like some of the referees liked it. So tomography is very hard to do but it's important and machine learning can help us to do that using neural networks and these to achieve mixed state tomography using an iterative eigenstate reconstruction. So why it is so challenging? Because you're trying to reconstruct the quantum States from measurements. You have a single qubit, you have a few Pauli matrices there are very few measurements to make when you have N qubits then the N appears in the exponent. So the number of measurements grows exponentially and this exponential scaling makes the computation to be very difficult. It's prohibitively expensive for large system sizes. So this is the bottleneck is these exponential dependence on the number of qubits. So by the time you get to 20 or 24 it is impossible. It gets even worst. Experimental data is noisy and therefore you need to consider maximum-likelihood estimation in order to reconstruct the quantum state that kind of fits the measurements best. And again these are expensive. There was a seminal work sometime ago on ion-traps. The post-processing for eight qubits took them an entire week. There were different ideas proposed regarding compressed sensing to reduce measurements, linear regression, et cetera. But they all have problems and you quickly hit a wall. There's no way to avoid it. Indeed the initial estimate is that to do tomography for 14 qubits state, you will take centuries and you cannot support a graduate student for a century because you need to pay your retirement benefits and it is simply complicated. So therefore a team here sometime ago we're looking at the question of how to do a full reconstruction of 14-qubit States with in four hours. Actually it was three point three hours Though sometime ago and many experimental groups were telling us that was very popular paper to read and study because they wanted to do fast quantum state tomography. They could not support the student for one or two centuries. They wanted to get the results quickly. And then because we need to get these density matrices and then they need to do these measurements here. But we have N qubits the number of expectation values go like four to the N to the Pauli matrices becomes much bigger. A maximum likelihood makes it even more time consuming. And this is the paper by the group in Inns brook, where they go this one week post-processing and they will speed-up done by different groups and hours. Also how to do 14 qubit tomography in four hours, using linear regression. But the next question is can machine learning help with quantum state tomography? Can allow us to give us the tools to do the next step to improve it even further. And then the standard one is this one here. Therefore for neural networks there are some inputs here, X1, X2 X3. There are some weighting factors when you get an output function PHI we just call Nonlinear activation function that could be heavy side Sigmon piecewise, linear logistic hyperbolic. And this creates a decision boundary and input space where you get let's say the red one, the red dots on the left and the blue dots on the right. Some separation between them. And you could have either two layers or three layers or any number layers can do either shallow or deep. This cannot allow you to approximate any continuous function. You can train data via some cost function minimization. And then there are different varieties of neural nets. We're looking at some sequel restricted Boltzmann machine. Restricted means that the input layer speeds are not talking to each other. The output layers means are not talking to each other. And we got reasonably good results with the input layer, output layer, no hidden layer and the probability of finding a spin configuration called the Boltzmann factor. So we try to leverage Pure-state tomography for mixed-state tomography. By doing an iterative process where you start here. So there are the mixed States in the blue area the pure States boundary here. And then the initial state is here with the iterative process you get closer and closer to the actual mixed state. And then eventually once you get here, you do the final jump inside. So you're looking at a dominant eigenstate which is closest pure state and then computer some measurements and then do an iterative algorithm that to make you approach this desire state. And after you do that then you can essentially compare results with some data. We got some data for four to eight trapped-ion qubits approximate W States were produced and they were looking at let's say the dominant eigenstate is reliably recorded for any equal four, five six, seven, eight for the ion-state, for the eigenvalues we're still working because we're getting some results which are not as accurate as we would like to. So this is still work in progress, but for the States is working really well. So there is some cost scaling which is beneficial, goes like NR as opposed to N squared. And then the most relevant information on the quality of the state production is retrieved directly. This works for flexible rank. And so it is possible to extract the ion-state within network tomography. It is cost-effective and scalable and delivers the most relevant information about state generation. And it's an interesting and viable use case for machine learning in quantum physics. We're also now more recently working on how to do quantum state tomography using Conditional Generative Adversarial Networks. Usually the masters student are analyzed in PhD and then two former postdocs. So this CGANs refers to this Conditional Generative Adversarial Networks. In this framework you have two neural networks which are essentially having a dual, they're competing with each other. And one of them is called generator another one is called discriminator. And there they're learning multi-modal models from the data. And then we improved these by adding a cost of neural network layers that enable the conversion of outputs from any standard neural network into physical density matrix. So therefore to reconstruct the density matrix, the generator layer and the discriminator networks So the two networks, they must train each other on data using standard gradient-based methods. So we demonstrate that our quantum state tomography and the adversarial network can reconstruct the optical quantum state with very high fidelity which is orders of magnitude faster and from less data than a standard maximum likelihood metals. So we're excited about this. We also show that this quantum state tomography with these adversarial networks can reconstruct a quantum state in a single evolution of the generator network. If it has been pre-trained on similar quantum States. so requires some additional training. And all of these is still work in progress where some preliminary results written up but we're continuing. And I would like to thank all of you for attending this talk. And thanks again for the invitation.

Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> We're back. You're watching theCUBE and our coverage of IBM Think 2020, the digital IBM thinking. We're here with Jamie Thomas, who's the general manager of strategy and development for IBM Systems. Jamie, great to see you. >> It's great to see you as always. >> You have been knee deep in qubits, the last couple years. And we're going to talk quantum. We've talked quantum a lot in the past, but it's a really interesting field. We spoke to you last year at IBM Think about this topic. And a year in this industry is a long time, but so give us the update what's new in quantum land? >> Well, Dave first of all, I'd like to say that in this environment we find ourselves in, I think we can all appreciate why innovation of this nature is perhaps more important going forward, right? If we look at some of the opportunities to solve some of the unsolvable problems, or solve problems much more quickly, in the case of pharmaceutical research. But for us in IBM, it's been a really busy year. First of all, we worked to advance the technology, which is first and foremost in terms of this journey to quantum. We just brought online our 53 qubit computer, which also has a quantum volume of 32, which we can talk about. And we've continued to advance the software stack that's attached to the technology because you have to have both the software and the hardware thing, right rate and pace. We've advanced our new network, which you and I have spoken about, which are those individuals across the commercial enterprises, academic and startups, who are working with us to co-create around quantum to help us understand the use cases that really can be solved in the future with quantum. And we've also continued to advance our community, which is serving as well in this new digital world that we're finding ourselves in, in terms of reaching out to developers. Now, we have over 300,000 unique downloads of the programming model that represents the developers that we're touching out there every day with quantum. These developers have, in the last year, have run over 140 billion quantum circuits. So, our machines in the cloud are quite active, and the cloud model, of course, is serving us well. The data's, in addition, to all the other things that I mentioned. >> So Jamie, what metrics are you trying to optimize on? You mentioned 53 qubits I saw that actually came online, I think, last fall. So you're nearly six months in now, which is awesome. But what are you measuring? Are you measuring stability or coherence or error rates? Number of qubits? What are the things that you're trying to optimize on to measure progress? >> Well, that's a good question. So we have this metric that we've defined over the last year or two called quantum volume. And quantum volume 32, which is the capacity of our current machine really is a representation of many of the things that you mentioned. It represents the power of the quantum machine, if you will. It includes a definition of our ability to provide error correction, to maintain states, to really accomplish workloads with the computer. So there's a number of factors that go into quantum volume, which we think are important. Now, qubits and the number of qubits is just one such metric. It really depends on the coherence and the effect of error correction, to really get the value out of the machine, and that's a very important metric. >> Yeah, we love to boil things down to a single metric. It's more complicated than that >> Yeah, yeah. >> specifically with quantum. So, talk a little bit more about what clients are doing and I'm particularly interested in the ecosystem that you're forming around quantum. >> Well, as I said, the ecosystem is both the network, which are those that are really intently working with us to co-create because we found, through our long history in IBM, that co-creation is really important. And also these researchers and developers realize that some of our developers today are really researchers, but as you as you go forward you get many different types of developers that are part of this mix. But in terms of our ecosystem, we're really fundamentally focused on key problems around chemistry, material science, financial services. And over the last year, there's over 200 papers that have been written out there from our network that really embody their work with us on this journey. So we're looking at things like quadratic speed up of things like Monte Carlo simulation, which is used in the financial services arena today to quantify risk. There's papers out there around topics like trade settlements, which in the world today trade settlements is a very complex domain with very interconnected complex rules and trillions of dollars in the purview of trade settlement. So, it's just an example. Options pricing, so you see examples around options pricing from corporations like JPMC in the area of financial services. And likewise in chemistry, there's a lot of research out there focused on batteries. As you can imagine, getting everything to electric powered batteries is an important topic. But today, the way we manufacture batteries can in fact create air pollution, in terms of the process, as well as we want batteries to have more retention in life to be more effective in energy conservation. So, how do we create batteries and still protect our environment, as we all would like to do? And so we've had a lot of research around things like the next generation of electric batteries, which is a key topic. But if you can think, you know Dave, there's so many topics here around chemistry, also pharmaceuticals that could be advanced with a quantum computer. Obviously, if you look at the COVID-19 news, our supercomputer that we installed at Oak Ridge National Laboratory for instance, is being used to analyze 8000 different compounds for specifically around COVID-19 and the possibilities of using those compounds to solve COVID-19, or influence it in a positive manner. You can think of the quantum computer when it comes online as an accelerator to a supercomputer like that, helping speed up this kind of research even faster than what we're able to do with something like the Summit supercomputer. Oak Ridge is one of our prominent clients with the quantum technology, and they certainly see it that way, right, as an accelerator to the capacity they already have. So a great example that I think is very germane in the time that we find ourselves in. >> How 'about startups in this ecosystem? Are you able to-- I mean there must be startups popping up all over the place for this opportunity. Are you working with any startups or incubating any startups? Can you talk about that? >> Oh yep. Absolutely. There's about a third of our network are in VC startups and there's a long list of them out there. They're focused on many different aspects of quantum computing. Many of 'em are focused on what I would call loosely, the programming model, looking at improving algorithms across different industries, making it easier for those that are, perhaps more skilled in domains, whether that is chemistry or financial services or mathematics, to use the power of the quantum computer. Many of those startups are leveraging our Qiskit, our quantum information science open programming model that we put out there so it's open. Many of the startups are using that programming model and then adding their own secret sauce, if you will, to understand how they can help bring on users in different ways. So it depends on their domain. You see some startups that are focused on the hardware as well, of course, looking at different hardware technologies that can be used to solve quantum. I would say I feel like more of them are focused on the software programming model. >> Well Jamie, it was interesting hear you talk about what some of the clients are doing. I mean obviously in pharmaceuticals, and battery manufacturers do a lot of advanced R and D, but you mentioned financial services, you know JPMC. It's almost like they're now doing advanced R and D trying to figure out how they can apply quantum to their business down the road. >> Absolutely, and we have a number of financial institutions that we've announced as part of the network. JPMC is just one of our premiere references who have written papers about it. But I would tell you that in the world of Monte Carlo simulation, options pricing, risk management, a small change can make a big difference in dollars. So we're talking about operations that in many cases they could achieve, but not achieve in the right amount of time. The ability to use quantum as an accelerator for these kind of operations is very important. And I can tell you, even in the last few weeks, we've had a number of briefings with financial companies for five hours on this topic. Looking at what could they do and learning from the work that's already done out there. I think this kind of advanced research is going to be very important. We also had new members that we announced at the beginning of the year at the CES show. Delta Airlines joined. First Transportation Company, Amgen joined, a pharmaceutical, an example of pharmaceuticals, as well as a number of other research organizations. Georgia Tech, University of New Mexico, Anthem Insurance, just an example of the industries that are looking to take advantage of this kind of technology as it matures. >> Well, and it strikes me too, that as you start to bring machine intelligence into the equation, it's a game changer. I mean, I've been saying that it's not Moore's Law driving the industry anymore, it's this combination of data, AI, and cloud for scale, but now-- Of course there are alternative processors going on, we're seeing that, but now as you bring in quantum that actually adds to that innovation cocktail, doesn't it? >> Yes, and as you recall when you and I spoke last year about this, there are certain domains today where you really cannot get as much effective gain out of classical computing. And clearly, chemistry is one of those domains because today, with classical computers, we're really unable to model even something as simple as a caffeine molecule, which we're all so very familiar with. I have my caffeine here with me today. (laughs) But you know, clearly, to the degree we can actually apply molecular modeling and the advantages that quantum brings to those fields, we'll be able to understand so much more about materials that affect all of us around the world, about energy, how to explore energy, and create energy without creating the carbon footprint and the bad outcomes associated with energy creation, and how to obviously deal with pharmaceutical creation much more effectively. There's a real promise in a lot of these different areas. >> I wonder if you could talk a little bit about some of the landscape and I'm really interested in what IBM brings to the table that's sort of different. You're seeing a lot of companies enter this space, some big and many small, what's the unique aspect that IBM brings to the table? You've mentioned co-creating before. Are you co-creating, coopertating with some of the other big guys? Maybe you could address that. >> Well, obviously this is a very hot topic, both within the technology industry and across government entities. I think that some of the key values we bring to the table is we are the only vendor right now that has a fleet of systems available in the cloud, and we've been out there for several years, enabling clients to take advantage of our capacity. We have both free access and premium access, which is what the network is paying for because they get access to the highest fidelity machines. Clearly, we understand intently, classical computing and the ability to leverage classical with quantum for advantage across many of these different industries, which I think is unique. We understand the cloud experience that we're bringing to play here with quantum since day one, and most importantly, I think we have strong relationships. We have, in many cases, we're still running the world. I see it every day coming through my clients' port vantage point. We understand financial services. We understand healthcare. We understand many of these important domains, and we're used to solving tough problems. So, we'll bring that experience with our clients and those industries to the table here and help them on this journey. >> You mentioned your experience in sort of traditional computing, basically if I understand it correctly, you're still using traditional silicon microprocessors to read and write the data that's coming out of quantum. I don't know if they're sitting physically side by side, but you've got this big cryogenic unit, cables coming in. That's the sort of standard for some time. It reminds me, can it go back to ENIAC? And now, which is really excites me because you look at the potential to miniaturize this over the next several decades, but is that right, you're sort of side by side with traditional computing approaches? >> Right, effectively what we do with quantum today does not happen without classical computers. The front end, you're coming in on classical computers. You're storing your data on classical computers, so that is the model that we're in today, and that will continue to happen. In terms of the quantum processor itself, it is a silicon based processor, but it's a superconducting technology, in our case, that runs inside that cryogenics unit at a very cold temperature. It is powered by next-generation electronics that we in IBM have innovated around and created our own electronic stack that actually sends microwave pulses into the processor that resides in the cryogenics unit. So when you think about the components of the system, you have to be innovating around the processor, the cryogenics unit, the custom electronic stack, and the software all at the same time. And yes, we're doing that in terms of being surrounded by this classical backplane that allows our Q network, as well as the developers around the world to actually communicate with these systems. >> The other thing that I really like about this conversation is it's not just R and D for the sake of R and D, you've actually, you're working with partners to, like you said, co-create, customers, financial services, airlines, manufacturing, et cetera. I wonder if you could maybe kind of address some of the things that you see happening in the sort of near to midterm, specifically as it relates to where people start. If I'm interested in this, what do I do? Do I need new skills? Do I need-- It's in the cloud, right? >> Yeah. >> So I can spit it up there, but where do people get started? >> Well they can certainly come to the Quantum Experience, which is our cloud experience and start to try out the system. So, we have both easy ways to get started with visual composition of circuits, as well as using the programming model that I mentioned, the Qiskit programming model. We've provided extensive YouTube videos out there already. So, developers who are interested in starting to learn about quantum can go out there and subscribe to our YouTube channel. We've got over 40 assets already recorded out there, and we continue to do those. We did one last week on quantum circuits for those that are more interested in that particular domain, but I think that's a part of this journey is making sure that we have all the assets out there digitally available for those around the world that want to interact with us. We have tremendous amount of education. We're also providing education to our business partners. One of our key network members, who I'll be speaking with later, I think today, is from Accenture. Accenture's an example of an organization that's helping their clients understand this quantum journey, and of course they're providing their own assets, if you will, but once again, taking advantage of the education that we're providing to them as a business partner. >> People talk about quantum being a decade away, but I think that's the wrong way to think about it, and I'd love your thoughts on this. It feels like, almost like the return coming out of COVID-19, it's going to come in waves, and there's parts that are going to be commercialized thoroughly and it's not binary. It's not like all of a sudden one day we're going to wake, "Hey, quantum is here!" It's really going to come in layers. Your thoughts? >> Yeah, I definitely agree with that. It's very important, that thought process because if you want to be competitive in your industry, you should think about getting started now. And that's why you see so many financial services, industrial firms, and others joining to really start experimentation around some of these domain areas to understand jointly how we evolve these algorithms to solve these problems. I think that the production level characteristics will curate the rate and pace of the industry. The industry, as we know, can drive things together faster. So together, we can make this a reality faster, and certainly none of us want to say it's going to be a decade, right. I mean, we're getting advantage today, in terms of the experimentation and the understanding of these problems, and we have to expedite that, I think, in the next few years. And certainly, with this arms race that we see, that's going to continue. One of the things I didn't mention is that IBM is also working with certain countries and we have significant agreements now with the countries of Germany and Japan to put quantum computers in an IBM facility in those countries. It's in collaboration with Fraunhofer Institute or miR Scientific Organization in Germany and with the University of Tokyo in Japan. So you can see that it's not only being pushed by industry, but it's also being pushed from the vantage of countries and bringing this research and technology to their countries. >> All right, Jamie, we're going to have to leave it there. Thanks so much for coming on theCUBE and give us the update. It's always great to see you. Hopefully, next time I see you, it'll be face to face. >> That's right, I hope so too. It's great to see you guys, thank you. Bye. >> All right, you're welcome. Keep it right there everybody. This is Dave Vellante for theCUBE. Be back right after this short break. (gentle music)

[Music] from the cube studios in Palo Alto in Boston it's the cube covering the IBM thing brought to you by IBM welcome back everybody you're watching the cube and our continuous coverage of IBM think Digital 20/20 events it's we've been wall-to-wall for a couple days now and and we bring in you all the action a bass fall is here here he is the global league for quantum education and open science at IBM quantum gave great to see you thanks for coming on yeah thanks for having me here Dave you're very welcome love the discussion on quantum but I gotta say so I'm reading your bio in your bio I see quantum algorithms experimental quantum computation nanoscale device fabrication cryogenic measurements and quantum software development hardware programming etc so you're obviously qualified to talk about quantum but but how how can somebody learn about quantum do I have to be like a rocket scientist then understand this stuff so Dave this is one of the things that I'm very passionate about it's also my job to make sure that anyone can learn about quantum computing today so primarily what I'm focused on is making sure that you don't need a PhD to program a quantum computer when I was going through my graduate studies trying to learn quantum computing I needed access to a lab so I have to go to graduate school to do this but in 2016 IBM put a quantum computer on the cloud in that dramatically changes the field it allows access to anyone from the world with just an internet connection to program a quantum computer so the question I'm trying to answer on a daily basis now is the question that you asked how do I learn to program a quantum computer well I'm trying to make several resources available for you to do that okay well let's talk about those resources I mean you have quantum you have access to quantum computers I talked to Jamie Thomas the other day she said that you guys it's all available in the IBM cloud I can't even I can't even imagine what the infrastructure behind that looks like but as a user I don't have to see that so how do I get access to this stuff so there are several quantum computers available on the cloud now and every time I think about this it's fascinating to me because I needed access to a lab to access these things but now you don't you can go to quantum computing dot ibm.com and get free access to several quantum computers now the question becomes if I give you this access to the quantum computers how do you learn to program them the software that you use to program them is called kiss kit just like we've made access to the quantum computers open for everyone our software is also open source you can access it by going to Kiska torgue that's QIS ki t org and if you go in particular to Kiska org slash education we've put together a textbook to help you go through everything that you'd learn in a classroom about quantum algorithms and to start programming the real quantum systems yourself so everything's ready for you to program immediately what was the it can you give me the quantity IBM want them - computing URL again yeah that's quantum - computing IBM com once you create an account there you immediately get access to several quantum computers which is an impressive thing to think about the cryogenics that you mentioned earlier the hardware the software all of it is ready for you to take advantage of but I gotta ask you I know it's sort of off topic here but but if I had to look under the covers I'm gonna see some big cryogenic unit with a bunch of cables coming in is that right that's exactly it very cold inside that's right so the way to here's the way to think about it outer space is about 200 times colder than room temperature and the temperature where the chip the quantum chips it's is another 200 times lower than that so we're talking very cold here we're talking only 15 Mille kelvins above absolute zero that's zero point zero one five degrees above absolute zero so it's a very cold system and you'd have several wires that are going down into this coil system to try to communicate with the quantum ship well and what's exciting to me about this whole thing Abe is it is it brings me back to the sort of the early days of computing and the you know huge rooms and now look where we are today and so I would expect that over the next many decades you're going to see sort of similar advanced advances in quantum and being able to actually execute at somewhat higher temperatures and in miniaturization it's very exciting time and we're really obviously at the very very early innings but I want to ask you just in terms of if if I'm a programmer and I'm a Java programmer can I actually come in and start using quantum if you what do I need to know to get started so you need to know two things the first thing is you need to be familiar with any programming language the easiest programming language to pick up today by far is Python so kiss kit is built based on Python so if you're able to quickly catch up with a few things in Python and we have a chapter dedicated to this topic in our textbook that's the first thing the second thing is simply having the ability to learn something new simply being excited about this field once you have those two together you can learn quantum computing very quickly within a few months the question then becomes catching up with the research and reading research papers that can take some time but for us to be able to talk through a quantum program takes only a few a few days of reading let's talk about what some of the folks are doing with quantum we talked again to Jamie Thomas and she gave me some examples not surprisingly you know you saw for instance some some examples in pharmaceutical and to the other obvious industries but then banking came in it's a but what what is it what are people doing with quantum today maybe you could add some color to that primarily most of the working quantum today is focused on understanding how to take problems in industry whether it is to understand how to simulate molecules whether it is to understand how to optimize a financial portfolio taking those problems and mapping them onto a quantum computer so that they can get solved so you'll see various various industries exploring how to take their problems and map onto a quantum computer so one one exciting one that I'm seeing a lot of progress in is chemistry learning how to simulate molecules using these quantum computers as someone with a physics background for me the exciting thing to see here is also how people are using these quantum computers which fundamentally are taking advantage of quantum mechanics to simulate other quantum systems so to understand nature better by using nature itself so this is another exciting progress that we're seeing in the field so exciting both from industry and from educational and science purpose so obviously it's a fascinating field and people would you say with curiosity it can get excited about it but but let's say I actually want you know some some kind of career in part of I mean what well how would people sort of get involved do you see you know on the horizon that this is gonna be something that is actually gonna be a vocation for you know young folks that want to get involved I could not tell you how challenging it is to find people who have the right combination of quantum computing knowledge and classical programming knowledge so in order to be able to take full advantage of the quantum systems today we need people who understand both the hardware and the software to some level and there is an extreme shortage of that kind of talent so the work that I'm focused on is exactly this problem of solving the workforce development problem so we're trying to make sure that people have access to anything that they need in order to be able to program a quantum computer and to learn how to then map their own problems into these quantum computers in the future the question becomes let's say we now understand how to use quantum computers to make financial portfolio optimization every bank in the world is going to want someone to implement this in their systems which immediately creates lots of jobs so this is going to become something that's in demand once it becomes possible on a on a large quantum computer so today is the right time to learn how to work with these quantum systems so that when the time comes that there are industries that are needing quantum skills you're ready to be hired for those positions okay so big skills gap you kind of gave an example in financial services where maybe some of the other things that you hope that that people are going to be able to do over time with these skills I cannot under I cannot over us overstate how important it is to learn how to simulate chemistry problems on these quantum computers that will have impacts anywhere ranging from whether it's drug design whether it's making better efficient solar panels more efficient batteries there are many applications where you'll see impact from these so the there are many industries that can benefit from understanding how to work with quantum computers that's something exciting I'm looking forward to see you know you read in the press that you know we're at least a decade away you know from from quantum being a reality but you're giving some examples where it's sort of here today I feel like it's going to come in layers you know not gonna be one big bang it's gonna come over time but but maybe you could you know frame that for us in terms of how you see this market developing I don't even want to call it a market but just this technology developing into a market what what has to take place and what kind of things can we expect along that journey sure so I think it's very important to keep in mind that quantum computers are fairly young technology so we're improving the technology as we go and there has been dramatic improvement in the technology itself but we're still learning as we go so one of the things that you'll find is that all of the applications work that's being done today is exploring how to take advantage of the quantum computer in some way if I immediately gave you a fully functional perfect quantum computer today you wouldn't even know what to do with it right you need to understand how to map problems on to that quantum computer so in preparation for that time several years away you'll see a lot of people trying to learn how to take advantage of quantum computers today and as they get better and better learning how to take advantage of whatever incremental progress is being made so as much as it seems like quantum computers are several years away many people are learning how to program them today just in preparation for that time when they're ready for use and my understanding is we're gonna get there with you know hybrid models today you're using you know traditional microprocessor technology to sort of read and write data from quantum that's likely going to continue for quite some time maybe maybe indefinitely but but but perhaps not right so Dave the important thing to remember is that a quantum computer works jointly with a classical computer if you ask me the question of how do i optimize my portfolio the numbers that I would need to compute with our classical there's nothing quantum about them these are numbers so there's classical information that you then have to take and map on to the quantum computer and then once the quantum computer is done you have to take the data out of that computer and then turn it back into classical information so you'll always have a quantum computer working jointly with a classical computer the question now is how do you make those two work together so that you can extract some benefit that you couldn't have attained with just the classic what do you see is the big sort of technical challenges that you're paying attention to you paying attention to I mean is it getting more you know qubits is a coherence working at higher temperatures what are the things that you see is as the the scientists are working on to move things forward so one of the things that I can do immediately Dave if you and I agreed right now is we can go to the lab and take a quantum chip and put a thousand cubits on that quantum chip that's fine we can do that immediately the problem that you'll find is that it doesn't matter that you have a thousand cubits if the qubits are not good quality cuteness so the technology should focus on improving the fundamental qualities of the qubits themselves before scaling them up to larger numbers in addition to that as you're scaling to larger and larger numbers new problems come into the picture so making better qubits scaling up seeing how the technology is doing learning new things and then scaling farther up that seems to be the model that's working today so in addition to monitoring the quality of the qubits themselves I'm monitoring within the technology how people are implementing solutions to scaling problems in addition to that another important problem that deserves a lot of attention is the question of how do you make good software that can take problems and map them onto quantum computers in in quantum computing when I say I'm running upon a program really what I'm doing is building a quantum circuit and then running that quantum circuit on the real device well if that circuit has certain operations in it maybe you want to tailor the way you transfer that circuit onto the device in a way that takes full advantage of the device itself but then in order to do that you need to write good software so improvements in the software along with improvements in the quantum technology itself will be how we get to success and at IBM we're focused on finding a metric that wraps all of these things together and it's called quantum volume and we're seeing improvements in the quantum volume of our systems as we go yeah Jamie talked about that you're essentially taking the key metrics and putting them into a you know a single observable metric that obviously you can track over time so I want to ask you about security a lot of people are concerned that the quantum is just going to blow away everything that we know cryptography and all the you know the the passwords and security systems that we we've put in place is that a legitimate concern will quantum you both get us into that problem and take us out of that that problem I wonder if you could talk about that so there are two ways to think about this problem one is just fundamentally if you ask me what does it take to put the the cryptography that has our bank accounts safe over the internet connections that we use it takes roughly about a thousand good cubits okay if I tell you a thousand good cubits that doesn't seem like a lot of work but when you think about it what it really requires is an overhead of about a thousand cubits for each qubit that we have today so the numbers of qubits that you need are in the millions in order to put the the kind of cryptography that we're using today at stake so certainly there's a long way to go that's one aspect of the story the other aspect of the story is that we should never underestimate the progress of technology so even though the time when we can use Shor's algorithm which is the algorithm that can be used to break the cryptographic algorithms like RSA even though that's several years away you still want to be ready for that time and what that means is if you have sensitive information today you need to be making sure that that information itself is protected with quantum resistant cryptographic techniques so that when the time comes you can't use a quantum computer to get back the data from today and break so two perspectives one is we're quite a while away from this kind of danger but at the same time it doesn't mean we should be complacent today we should be taking preparations make sure that our critical information is protected yeah that's so that that makes a lot of sense but when you say we're a ways away or we are we decades away we years away we can you and you quantify that in any reasonable way it's hard to speculate on that number so I'll refrain from giving you a specific timeline just to give you an idea the quantum bits that were in development ten years ago had a coherence time so the amount of time that they can store the quantum information of roughly a hundred times smaller than they are today and ten years ago if you asked people how do we get to a hundred times better qubits nobody would have been able to give you a clear answer you could have guessed some ways but nobody would have been able to tell you we'll get there in ten years but we did so instead of coming up with estimates of timelines that depend on what we know today it's probably a better idea to monitor the technology as it goes and keep adapting we're probably talking this century where we're talking to the century hopefully it is my last mission to enable enough people to learn quantum such that it happens within my life very exciting field a I can't thank you enough for helping us educate the audience and and my and myself personally really I'm I'm so fascinated by this it's something that you know jumper and I and the team have been really focused on and I think it's really time to your point the start digging and start learning you've given us some resources there give us give them give us those two reasons one more time there's there's the IBM site and the the the the the queue kit site use that site what are those again just those to wrap so you can access the quantum computers at quantum - computing ibm.com and once you're there the way to learn how to program these quantum computers is by using kiss kit which you can learn about by going to kiss kit org slash education once here at that education page you can access our textbook which we make open-source it's a textbook that's co-written with professors in the field and is open source so it's continually getting updated you can access that textbook at tisket org slash textbook if you go to our youtube channel you'll find several videos that allow you to also learn very quickly so kiss gets YouTube channel is another great place to look so lots of resources and that's kiss kit with a Q which is why I wrote it that way so alright exact thanks so much it was great to see you stay safe and next time hopefully we'll see you face-to-face and you can draw some some cool pictures to help me understand this even better Dave it was nice talking with you I look forward to learning quantum programming with you yeah Cheers and thank you for watching everybody this is the cubes coverage of the IBM think 2020 digital event experience we'll be right back Brennan for this short break [Music] you