>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020, sponsored by Intel and AWS. >>Welcome back to the queue. It's virtual coverage of Avis reinvent 2020 I'm John furry, your host. Um, this is a cute virtual we're here. Not in, in remote. We're not in person this year, so we're doing the remote interviews. And then this segment is going to build on the quantum conversation we had last year, Richard moles, general manager of Amazon bracket and aid was quantum computing and Fernando Brandao head of quantum algorithms at AWS and Brent professor of theoretical physics at Caltech. Fernando, thanks for coming on, Richard. Thanks for joining us. >>You're welcome to be here. >>So, Fernando, first of all, love your title, quantum algorithms. That's the coolest title I've heard so far and you're pretty smart because you're a theoretical professor of physics at Caltech. So, um, which I'd never be able to get into, but I wish I could get into there someday, but, uh, thanks for coming on. Um, quantum has been quite the rage and you know, there's a lot of people talking about it. Um, it's not ready for prime time. Some say it's moving faster than others, but where are we on quantum right now? What are, what are you, what are you seeing Fernanda where the quantum, where are peg us in the evolution of, of, uh, where we are? >>Um, yeah, what quantum, uh, it's an emerging and rapidly developing fields. Uh, but we are see where are you on, uh, both in terms of, uh, hardware development and in terms of identifying the most impactful use cases of one company. Uh, so, so it's, it's, it's early days for everyone and, and we have like, uh, different players and different technologies that are being sport. And I think it's, it's, it's early, but it's exciting time to be doing quantum computing. And, uh, and it's very interesting to see the interest in industry growing and, and customers. Uh, for example, Casa from AWS, uh, being, uh, being willing to take part in this journey with us in developmental technology. >>Awesome. Richard, last year we talked to bill Vass about this and he was, you know, he set expectations really well, I thought, but it was pretty much in classic Amazonian way. You know, it makes the announcement a lot of progress then makes me give us the update on your end. You guys now are shipping brackets available. What's the update on your end and Verner mentioned in his keynote this week >> as well. Yeah, it was a, it was great until I was really looking at your interview with bill. It was, uh, that was when we launched the launch the service a year ago, almost exactly a year ago this week. And we've come a long way. So as you mentioned, we've, uh, we've, uh, we've gone to general availability with the service now that that happened in August. So now a customer can kind of look into the, uh, to the bracket console and, uh, installed programming concept computers. You know, there's, uh, there's tremendous excitement obviously, as, as you mentioned, and Fernando mentioned, you know, quantum computers, uh, we think >>Have the potential to solve problems that are currently, uh, uh, unsolvable. Um, the goal of bracket is to fundamentally give customers the ability to, uh, to go test, uh, some of those notions to explore the technology and to just start planning for the future. You know, our goal was always to try and solve some of the problems that customers have had for, you know, gee, a decade or so now, you know, they tell us from a variety of different industries, whether it's drug discovery or financial services, whether it's energy or there's chemical engineering, machine learning, you know, th the potential for quantum computer impacts may industries could potentially be disruptive to those industries. And, uh, it's, it's essential that customers can can plan for the future, you know, build their own internal resources, become experts, hire the right staff, figure out where it might impact their business and, uh, and potentially disrupt. >>So, uh, you know, in the past they're finding it hard to, to get involved. You know, these machines are very different, different technologies building in different ways of different characteristics. Uh, the tooling is very disparate, very fragmented. Historically, it's hard for companies to get access to the machines. These tend to be, you know, owned by startups or in, you know, physics labs or universities, very difficult to get access to these things, very different commercial models. Um, and, uh, as you, as you suggested, a lot of interests, a lot of hype, a lot of claims in the industry, customers want to cut through all that. They want to understand what's real, uh, what they can do today, uh, how they can experiment and, uh, and get started. So, you know, we see bracket as a catalyst for innovation. We want to bring together end-users, um, consultants, uh, software developers, um, providers that want to host services on top of bracket, try and get the industry, you know, rubbing along them. You spoke to lots of Amazonians. I'm sure you've heard the phrase innovation flywheel, plenty of times. Um, we see the same approach that we've used successfully in IOT and robotics and machine learning and apply that same approach to content, machine learning software, to quantum computing, and to learn, to bring it together. And, uh, if we get the tooling right, and we make it easy, um, then we don't see any reason why we can't, uh, you know, rapidly try and move this industry forward. And >>It was fun areas where there's a lot of, you know, intellectual computer science, um, technology science involved in super exciting. And Amazon's supposed to some of that undifferentiated heavy. >>That's what I am, you know, it's like, >>There's a Maslow hierarchy of needs in the tech industry. You know, people say, Oh, why five people freak out when there's no wifi? You know, you can't get enough compute. Right. So, you know, um, compute is one of those things with machine learning is seeing the benefits and quantum there's so much benefits there. Um, and you guys made some announcements at, at re-invent, uh, around BRACA. Can you share just quickly share some of those updates, Richard? >>Sure. I mean, it's the way we innovate at AWS. You know, we, we start simple and we, and we build up features. We listen to customers and we learn as we go along, we try and move as quickly as possible. So since going public in, uh, in, in August, we've actually had a string of releases, uh, pretty consistent, um, delivering new features. So we try to tie not the integration with the platform. Customers have told us really very early on that they, they don't just want to play with the technology. They want to figure out how to, how to envisage a production quantum computing service, how it might look, you know, in the context of a broad cloud platform with AWS. So we've, uh, we launched some integration with, uh, other AWS capabilities around security, managing limits, quotas, tagging resources, that type of thing, things that are familiar to, uh, to, to, to current AWS users. >>Uh, we launched some new hardware. Uh, all of our partners D-Wave launched some, uh, uh, you know, a 5,000 cubit machine, uh, just in September. Uh, so we made that available on bracket the same day that they launched that hardware, which was very cool. Um, you know, we've made it, uh, we've, we've made it easier for researchers. We've been, you know, impressed how many academics and researchers have used the service, not just large corporations. Um, they want to have really deep access to these machines. They want to program these things at a low level. So we launched some features, uh, to enable them to do their research, but reinvent, we were really focused on two things, um, simulators and making it much easier to use, uh, hybrid systems systems that, uh, incorporate classical compute, traditional digital computing with quantum machinery, um, in the vein that follow some of the liens that we've seen, uh, in machine learning. >>So, uh, simulators are important. They're a very important part of, uh, learning how to use concepts, computers. They're always available 24, seven they're super convenient to use. And of course they're critical in verifying the accuracy of the results that we get from quantum hardware. When we launched the service behind free simulator for customers to help debug their circuits and experiments quickly, um, but simulating large experiments and large systems is a real challenge on classical computers. You know, it, wasn't hard on classical. Uh, then you wouldn't need a quantum computer. That's the whole point. So running large simulations, you know, is expensive in terms of resources. It's complicated. Uh, we launched a pretty powerful simulator, uh, back in August, which we thought at the time was always powerful managed. Quantum stimulates circuit handled 34 cubits, and it reinvented last week, we launched a new simulator, which actually the first managed simulator to use tensor network technology. >>And it can run up to 50 cubits. So we think is, we think is probably the most powerful, uh, managed quantum simulator on the market today. And customers can flip easily between either using real quantum hardware or either of our, uh, stimulators just by changing a line of code. Um, the other thing we launched was the ability to run these hybrid systems. You know, quantum computers will get more, no don't get onto in a moment is, uh, today's computers are very imperfect, you know, lots of errors. Um, we working, obviously the industry towards fault-tolerant machines and Fernando can talk about some research papers that were published in that area, but right now the machines are far from perfect. And, uh, and the way that we can try to squeeze as much value out of these devices today is to run them in tandem with classical systems. >>We think of the notion of a self-learning quantum algorithm, where you use a classical optimization techniques, such as we see machine learning to tweak and tune the parameters of a quantum algorithm to try and iterate and converge on the best answer and try and overcome some of these issues surrounding errors. That's a lot of moving parts to orchestrate for customers, a lot of different systems, a lot of different programming techniques. And we wanted to make that much easier. We've been impressed with a, a, an open projects, been around for a couple of years, uh, called penny lane after the Beatles song. And, um, so we wanted to double down on that. We were getting a lot of positive feedback from customers about the penny lane talk it, so we decided to, uh, uh, make it a first class citizen on bracket, make it available as a native feature, uh, in our, uh, in our Jupiter notebooks and our tutorials learning examples, um, that open source project has very similar, um, guiding principles that we do, you know, it's open, it's cross platform, it's technology agnostic, and we thought he was a great fit to the service. >>So we, uh, we announced that and made it available to customers and, uh, and, and, uh, already getting great feedback. So, uh, you know, finishing the finishing the year strongly, I think, um, looking forward to 2021, you know, looking forward to some really cool technology it's on the horizon, uh, from a hardware point of view, making it easy to use, um, you know, and always, obviously trying to work back from customer problems. And so congratulations on the success. I'm sure it's not hard to hire people interested, at least finding qualified people it'd be different, but, you know, sign me up. I love quantum great people, Fernando real quick, understanding the relationship with Caltech unique to Amazon. Um, tell us how that fits into the, into this, >>Uh, right. John S no, as I was saying, it's it's early days, uh, for, for quantum computing, uh, and to make progress, uh, in abreast, uh, put together a team of experts, right. To work both on, on find new use cases of quantum computing and also, uh, building more powerful, uh, quantum hardware. Uh, so the AWS center for quantum computing is based at Caltech. Uh, and, and this comes from the belief of AWS that, uh, in quantum computing is key to, uh, to keep close, to stay close of like fresh ideas and to the latest scientific developments. Right. And Caltech is if you're near one computing. So what's the ideal place for doing that? Uh, so in the center, we, we put together researchers and engineers, uh, from computer science, physics, and other subjects, uh, from Amazon, but also from all the academic institutions, uh, of course some context, but we also have Stanford and university of Chicago, uh, among others. So we broke wrongs, uh, in the beauty for AWS and for quantum computer in the summer, uh, and under construction right now. Uh, but, uh, as we speak, John, the team is busy, uh, uh, you know, getting stuff in, in temporary lab space that we have at cottage. >>Awesome. Great. And real quick, I know we've got some time pressure here, but you published some new research, give a quick a plug for the new research. Tell us about that. >>Um, right. So, so, you know, as part of the effort or the integration for one company, uh, we are developing a new cubix, uh, which we choose a combination of acoustic and electric components. So this kind of hybrid Aquacel execute, it has the promise for a much smaller footprint, think about like a few microliters and much longer storage times, like up to settlements, uh, which, which is a big improvement over the scale of the arts sort of writing all export based cubits, but that's not the whole story, right? On six, if you have a good security should make good use of it. Uh, so what we did in this paper, they were just put out, uh, is, is a proposal for an architecture of how to build a scalable quantum computer using these cubits. So we found from our analysis that we can get more than a 10 X overheads in the resources required from URI, a universal thought around quantum computer. >>Uh, so what are these resources? This is like a smaller number of physical cubits. Uh, this is a smaller footprint is, uh, fewer control lines in like a smaller approach and a consistent, right. And, and these are all like, uh, I think this is a solid contribution. Uh, no, it's a theoretical analysis, right? So, so the, uh, the experimental development has to come, but I think this is a solid contribution in the big challenge of scaling up this quantum systems. Uh, so, so, so John, as we speak like, uh, data blessed in the, for quantum computing is, uh, working on the experimental development of this, uh, a highly adequacy architecture, but we also keep exploring other promising ways of doing scalable quantum computers and eventually, uh, to bring a more powerful computer resources to AWS customers. >>It's kind of like machine learning and data science, the smartest people work on it. Then you democratize that. I can see where this is going. Um, Richard real quick, um, for people who want to get involved and participate or consume, what do they do? Give us the playbook real quick. Uh, so simple, just go to the AWS console and kind of log onto the, to the bracket, uh, bracket console, jump in, you know, uh, create, um, create a Jupiter notebook, pull down some of our sample, uh, applications run through the notebook and program a quantum computer. It's literally that simple. There's plenty of tutorials. It's easy to get started, you know, classic cloud style right now from commitment. Jump in, start simple, get going. We want you to go quantum. You can't go back, go quantum. You can't go back to regular computing. I think people will be running concert classical systems in parallel for quite some time. So yeah, this is the, this is definitely not a one way door. You know, you go explore quantum computing and see how it fits into, uh, >>You know, into the, into solving some of the problems that you wanted to solve in the future. But definitely this is not a replacement technology. This is a complimentary technology. >>It's great. It's a great innovation. It's kind of intoxicating technically to get, think about the benefits Fernando, Richard, thanks for coming on. It's really exciting. I'm looking forward to keeping up keeping track of the progress. Thanks for coming on the cube coverage of reinvent, quantum computing going the next level coexisting building on top of the shoulders of other giant technologies. This is where the computing wave is going. It's different. It's impacting people's lives. This is the cube coverage of re-invent. Thanks for watching.