 Okay, so We're excited to get started this year We have been working on a number of partnerships to advance quantum computational science to solve valuable problems So today we're going to take some time to talk a little bit about how these collaborations work so that we can solve problems For quantum computational science Okay, so if my panelists here today and let's kick off the conversation. I think Travis I'm going to start with you. I want to know what do you think about how? Classical and quantum computing will work together to solve problems for materials and other Use cases as well. Oh, thanks a lot. Jamie. And it's a pleasure to be here For those who don't know Oak Ridge National Laboratory is one of the Department of Energy's research and development centers Located in Tennessee and we are very interested in the material science issue Both for the types of materials that we think can advance energy security But also scientific discovery for its own right now We are home to the Spalation neutron source the brightest source of neutrons in the world as well as currently the world's fastest supercomputer known as frontier and I want to focus on that Computing resource for the moment because using computation to understand and discover new principles of science is fundamental to what we do both as scientists but also as Part of the Department of Energy's mission What is critical here is that quantum computing appears to provide a new tool a platform in which we can enable new types of material science Discovery we find that some of the hardest problems correspond to strongly correlated systems This could either be electrons a high-energy particles other types of spins in Order to accurately simulate those systems We do need new tools to address the types of scalable entanglement and coherence that arises in these materials in real life Now I see this intersection therefore as a Merging of how we use currently high-performance computing systems to solve some of these problems with this new accelerator capability Quantum computers. I'll give you a few examples of where I think this can happen One is that we can directly try to simulate these materials almost use a quantum computer to Create a synthetic or computational material a model that actually mimics the actual behavior of the systems that we're trying to Characterize experimentally. That's a great direct brute-force approach to how to use a quantum computer An alternative is to actually think about how we balance the workflow Maybe embed a computation in the quantum computer that our classical computer can't solve efficiently This could be a matter of scale as we look at the granularity of these types of material simulations The smaller we get in terms of energy and length scale the more difficult it becomes to capture those strongly correlated dynamics So Jamie maybe just to get to your point that Material science is absolutely a fundamentally exciting area and quantum computing appears to offer a really novel approach to how to address that Fantastic, I think that's great. I think that leads very nicely into my next question. That's for Kenny Kenny I want to know a little bit more about what barriers of adoption you see For quantum computing in the health care and life sciences and what are some of the strategies that we can you know put in place to drive usage and Adoption. Yeah, thanks. Thanks for that question. I mean, it's absolutely been wonderful to be here. I've learned so much about what's going on in this community and Excited to have the opportunity to tell you a little bit about what's going on in health care and life sciences I'm at the Cleveland Clinic and I'm also involved in a white paper with a group of IBM investigators and Colleagues around the world to try to like flesh out ideas for use cases in Computation in health care and life sciences. I've also been in the field for 30 or 40 years So I have a lot of my own ideas and things I want to pursue also in discussions with colleagues from the Cleveland Clinic and Just at lunch. We had a wonderful conversation with some colleagues at a pharmaceutical company about What they think, you know, which is really the important aspect of this and just I came here yesterday from a meeting in Spain and there's a lot of conversations about high-resolution crystallography and Had long conversations with colleagues there about how quantum computing could potentially facilitate our understanding of these very Complicated protein ligand systems or or structures in material structures even So like the main things that come up constantly in my discussions with colleagues in the field are really a Quantum chemistry is sort of the primary one right in computational chemistry or biology in my field It's sort of the foundational method So people are real excited about you know advances in quantum chemistry and how that could be used to say build better Potentials create data sets to use machine learning and AI for example the quantum crystallographer He wanted to use quantum chemistry and quantum computing to Refine these crystal structures of protein in complexes So that's one area that that comes up all the time is you know, how can we take advantage of this? Another one of course is it won't be any surprise to anybody if you're you know have been following the literature recently is Machine learning and AI so what can machine learning and AI? What can quantum computing do to accelerate build better models build them faster? This is something and this came up at lunch today In detail that they're you know very interested in using these kinds of things So what are the barriers right and and to me what was really exciting about the morning session is that? Some of these issues with the regards to the barriers were being addressed right? There's an educational component, but you need to live where the scientists are right? And so in the health care life sciences we have user interfaces We have gooies you know people are very very familiar with these and so the challenge is like okay now You're telling me I have to learn another user interface And so I think bringing kind of these tools to the the users to their user interface that they're very comfortable with I think it's really important Obviously you could you know train them to use quiz kit, but I think for really rapid fast adoption You really want to have it kind of in their workspace if you will So you know training is a really important component of this area so in terms of You know my field. I think the barriers are training. Oh, and sorry the other point That came up even at lunch is the killer application, right? What is the application that's going to really address a key fundamental problem in health care and life sciences? And if you indeed create something that say every pharmaceutical company in the world needs in their workflow They will adapt and adopt these kinds of technologies very rapidly So to really answer the question I think we need and I think John will talk a little bit more about training better training bringing the technology to the User and developing those key applications you're understanding the workflow and then building teams that are capable of Going from the quantum information science all the way to the application So it could be an experimentalist You know could be a computational chemist or biologist and then a quantum information scientist So I think those are really kind of the challenges that we face in our field And then these workflows that you're referencing could be Combination of classical with quantum. Yes. Absolutely. Similar to what yeah getting what Travis is saying And I think most you know even the conversation today most of the discussion was yeah You know we have a workflow and there's one step that is you know slow and really amenable to a quantum device That's the one we're going to focus on accelerating And you know sort of what Chavez is saying like having this kind of mixed environment Where are you going to use a quantum computing when it's really needed and not use it for say IO or something like that Right and that it's classically challenging. Right. Yeah, Jamie if I can follow on to this because sure like you're saying Kenny There are decades of existing methodology and data sets And standards for how these workflows are supposed to be executed and you can't throw that out just because a new technology came along So it fundamentally is about integration, right? Yeah Really good point. Yeah, there are standards, right? So expectations for certain problem solving Okay, so that I think leads us nicely into our next question. So I wanted to ask Sophia What do you see in terms of the potential for near term quantum computing for the high energy physics area? So, uh, I think high energy physics is one of those fields that really has Very very big computational challenges ahead So you you heard a little bit about us, uh during the hand of the morning sessions So fundamental fundamental questions about the universe are what we are trying to answer right with high energy physics And cern is one of the main But this is happening today. It's the biggest one for sure Now the point is that we are we know already that The computational models we use so far will only You know allow us to do Basically the next 10 years physics But if we look into the next generation colliders, that's won't be enough And so what we really need to do is to start looking for new technologies And that's why we started looking into quantum already a few years ago And what we are seeing is that there are quite a few problems that we can start solving even today Those are still relatively small scale But they are very very important results because they tell us they show that we can use quantum computers to do simulation for example So being able to simulate Our theoretical models will give us You know information about what are the next Areas that we should survey with our detectors At the same time we can use quantum computers to speed up and improve data processing So this is a very very big challenge for cern and the energy physics community in general The point is that you've probably seen pictures of our detectors. Those are very big 3d Cameras okay, and and so what you need to be able to to make sense of is millions and millions of sensors Which in fact are needed to to to reproduce physics quantities And so all of those physics quantities they have quantum nature behind that And that's why we can actually Make sense of them using quantum algorithms in a way that is very very powerful even today now Size is still a problem But those exciting news we heard about today with the announcements at the roadmap They really make us think we're getting closer and closer to very realistic application very soon And this is something that's very important for us. So typically The way we solve our problems is is really using multi step multi layered algorithms We go from analyzing data in one detector to the next one and the next one So this is typically a work A workload or an algorithm or a set of algorithms that can easily map to this idea of having quantum computing and hpc Integrated even over a short term. Okay, so this is really something that we think we'll be able to To use for realistic applications very soon Now the other thing the other point I wanted to make is also that Uh, we measure lifetime of our experiments in decades. Okay, right now. We have a program that is set I would say for the end of 2040 2050, okay It's pretty pretty far away in time, but we are already trying to plan what to do next Okay, so this is if you think about it, it's a very long time scales that allows us also to dream a little bit So one thing one idea I wanted to push and just we are really leveraging my, you know experimental physicist side Is wondering whether we could see at some point combination between computing and quantum Sensing in such a way that we could really build integrated detectors of unprecedented accuracy So this is kind of a dream a very long term dream That would make a big difference in our field Yeah, so amazing the time scales that you guys are working on in terms of the experiments and getting ready for Preparing for it ahead of time And so in that way you do have the chance to plan ahead for like the devices of the future Yes, and I think I mean one of the important thing we learned about this exercise this idea of building working groups Dedicated to this to this domain This is really something that helped us prioritize and look at problems in a In a different way than what we typically do when we are trying to solve them daily, right? So it allowed us to talk to other communities Theoreticians and experimentalists really could work together In a way that you know, it doesn't always happen. So Efficiently daily, I would say and this really helped us prioritize and on the future use cases Yeah, and the other thing I really like about you know, some of the discussions we've had is you've talked about using quantum computers as an instrument Yes, and so it's just it's another way of thinking about how you might actually use quantum computing for Hydrogen physics. Yes. So so as I said the idea of so The instrument saw something at the sensor a part of the detector that will really capture The interaction of a particle the the the the information that we are trying to learn that is something that is extremely exciting. Yeah, yeah Excellent. Thank you so much. Sophia. So um, I'm I think going to move to john and ask a question to john on what does uh What does utility mean? to rpi And its ecosystem Terrific And thanks for the question And this is great. This morning was great listening to all the the things that are coming along It's wonderful with colleagues like this that thinking long term in terms of what do we do? Let me back up a little bit rpi is the oldest technological university in this country in the us And it was originally founded with the motto of Application of science for the common purposes of life And I think quantum is application of science for the common purposes of life. So we're just continuing on in that mission Similar to what the other three speakers have talked about We have a very large high performance computing System about an eight petaflop system on campus. So we've been in hbc for a while I think we'll see some of those techniques move. We'll see some of those techniques not move to quantum and How do we have a composable environment? That we can help educate the next generation And kenny alluded to it, but I I I think one of the real keys here is how do we get the next generation really excited About this, you know, like, you know, the excitement we have in this room and and we've all been vested in this But how do we get this next generation excited about quantum computing and and really investing themselves? I can tell you on our campus We're in the process of putting a system one in place that'll be operational on march Um, and I can't go just about anywhere on campus without a group of undergraduates as soon as I say the word quantum They're swarming all around me and it's just wonderful. It's wonderful what they they want to do and What they see for the future. I'm not sure they have the 10 or 20 or 30 year view that sophia has But they have about the two or three day view, but uh, that's okay. We'll we'll work with that and try to Manage that excitement and that passion And they're going to teach us a few things as they go through a learning experiment Experiments of things that maybe we wouldn't try because we're we're farther along the path if if you will, but I'm very excited about what we can do with the next generation If you look at rpi we're about 85 percent science and engineering in our student body And about half of that are pretty intensive in computing computer science it computer systems engineering. So these are the folks that You know, we'd like to get really excited about helping the ibm's and partners and others Figure things out as as we go through this and We already have it's kind of amusing But we already have a club the students the undergraduate students have already formed an a quantum computing club, which I think I shouldn't say this to this group, but sounded a little nerdy to me, but But they already got funding and they meet once a week and they talk quantum for an hour and I Didn't think that was my college experience, but that is their college experience, which is great And uh, yeah, how do we how do we keep harnessing that keep encouraging them to go forward? I mentioned to all of you yesterday one of the questions I'm asked all the time is By the undergraduates is Can I touch it and can I access it and the answer is no and maybe No, you can't touch it, but maybe if you get Connected with one of our faculty members or one of our researchers We'll get you access to the one of the quantum cues and get you going and see what you can do And once again, they'll probably teach us a few things along the way But if I make Just add into this. I think our our approachable this technology is becoming how you're making it available on the cloud How now we are going we've seen ai for building for building cascade programs This is something that's going to capture more and more attention more and more enthusiasm in the younger generations We are going to be flooded with requested of you know students coming to work on these kind of topics Need to get ready. Yeah So there it sounds like maybe I'm seeing a lot of nodding heads a lot of enthusiasm from Students and and the future generation I guess of quantum computational sciences Jamie I absolutely think we're in extremely creative period in the history of quantum computing right now. I mean it is Maybe not a golden age. That seems like we got to wait before we use that phrase, but um, it's a historical Historical. Yeah. Yeah. We'll come back to that But but the number of opportunities that are available both on the technology side on the application side These these people know this right we know this that's why we're all here And how we best take advantage of that I think at least from my perspective Is something that I reflect on every day Because I can be quick to adopt these new types of techniques think about these workflows how I want to integrate them in there But oftentimes if we're not getting the feedback You know or providing that feedback to the vendors and and to the other end users and developers Then we can find ourselves wandering down, you know endless nameless roads So this creativity comes with a risk, right? Which is we've got to learn how to balance our enthusiasm with the opportunities that are out there And we can only do that when we have enough people enough critical mass to enable that type of uh collaboration Right. So when I just kind of like in in my career, you know as a grad student I start out with single processor You know cpus and then my advisor got a call from a company called cre research I don't know how many of you remember cre research But they said they had this vector machine and we got our code going and it was 500 times faster And then you know a few years later gpus come online. It's 500 times faster, you know, so I think You know, you're not going to fool me this time. I want to be prepared when quantum computing, you know has this huge You know jump in performance for certain applications. So really to follow on kind of your your comment, I think Yeah, yeah, if I could add to uh for us, uh, we have things called core requirements that go across all of our Various curricula and for instance communications requirements things like that But we have something right now called data dexterity Where you have to have two courses that have some level of manipulating data doing data analytics and so on And I would see uh, I don't want to get ahead of the faculty But I I expect that we'll have several courses that are required in quantum awareness for all of our students And and this is going to change the way they learn and also the way we teach in terms of what's next And I I mentioned just yesterday, but it's a ways back, but I always I always remember this example of When we first required Mobile computing laptops for all of our students one of our faculty members came up to me and she said well You know I used to always teach the ideal gas law And by the way, I thought the ideal gas law was something that had to be taught to everybody And she said but what's what's interesting in the ideal gas law? There's all these assumptions that aren't quite correct We just we simplified it to be able to get people to start to understand All this and she said now with the laptops I use A real gas and I have them work on a real gas hydrogen in this case And we start there and then move on from that and I was like well, boy That just that just changed the game from her perspective and how she was teaching her course Which I think that'll happen here too. Yeah, I wonder how much like of this creativity rate is actually, you know Spurred on by bringing together a mixture of different perspectives and people taking a new lens on on something on a problem So bringing together like classical and quantum with industry experts What have you guys seen in terms of like the real benefits in terms of that multidisciplinary kind of aspect as well as the Collaborations that we set up for quantum in particular in advancing quantum science Yeah, so I haven't met a person yet. That's a quantum information science expert a computational chemistry You know computer a drug design expert I think really the the kinds of problems we're going after are so multidisciplinary You absolutely have to collaborate right and you know in the group. I'm building in the cleven clinic You know, I'm really setting up little mini teams where you're going to have like a quantum information scientist Say a quantum chemist and then maybe someone in computer a drug design And then try to get them to talk to each other and the other problem is common language Right and so, you know get them to sort of work together develop a common language to attack a fundamental problem And I think if you just had Say just the Structure a drug design person. They wouldn't be able to make as much progress. So I think really collaboration and you know collaboration with other companies other groups Also, I think it's going to be really really critical and moving this field forward But it's also the point of view right because if you want to build good algorithms for your problems You need to be able to formulate your problem in a way that is suitable and it's often very different How you do classically so you need to take a step back and get you know someone that maybe has a different view on things to To help your you know just the interaction with this different way of doing things can help that direction Otherwise, you're always repeating and maybe just you know trying to translate from classical to quantum Doesn't really make sense right right and you'd also bring in the software vendors I think you know if you have a certain user interface, you know, both the method developers, but also Packaging in such a way that it can be used at scale and by other people with you know Not you know extensive understanding of maybe all the technical details, right? They just really want to solve their problem Which is fine Okay. Yeah, and curious that i'll just ask out one more question. Um, what does utility mean to each of you? Maybe we can just go down So you did utility uh from what i've seen this morning You know something that i said at the beginning when i started talking and the fact that When we need to process our data we need to go through a lot of different steps And some of those are very efficient some are not so efficient some are not so elegant Some of them introduce biases according to what we already know and expect and maybe they are not the best Now utility from what i've seen today means that maybe we could think of skipping all of those steps And imagine a moment in which which is not so far away in time at all In which we could just take the output of our detectors and get out You know get it through a quantum algorithms and get out the physics quantities we need And then we can make our discovery and this is uh, this is Very nice concept of utility Yeah, i think i have a similar um definition though i may not be able to say it quite as nicely i i might Pose it instead as an example of how i want to use Utility in scientific discovery and i had mentioned earlier You know this idea of using the quantum computer as like a proxy for the material system And if i can do that then i can program that computer so it mimics whatever type of material i want It could be something that i have synthesized In the laboratory or it could be something that i can't synthesize in the laboratory I just don't know how to do it think of like a room temperature superconductor And then i go off and i try to create that material and i do a characterization of that material using my microscopes and my neutrons and my x-rays and all these types of things and i look at that data and i come back and i look at my quantum simulation of that model and it shows that they are Either in agreement or disagreement But i trust the quantum computer to the point that it's now informing my decision About what's actually happening in that material and how that material behaves and now that utility gets You know steeped in my definition of discovery and innovation so I think of that as an example of what it means to me Is this enablement of a new type of uh paradigm for scientific innovation Awesome. Thank you. You're kidding. Yeah, so utility to me You know once this um quantum devices gets so ubiquitous that it becomes just a standard tool in my workflow So like i have a problem and i'm a method developer, right? I want to develop algorithms and workflows and methods to solve problems and particularly in chemistry and biology, right? And so when i can identify a problem and say okay These are the steps and this happens this step here happens to be perfect You know perfect for a quantum device So i may use gpu's here and then i'll call this off and so utility is when it becomes fully integrated in my Uh computational toolkit to solve problems of interest to me You know very similar to uh, the rest of the comments although going back to this morning's Racing car analogy, which I thought was great First and foremost, I think I want the car to start up when I say start up Uh, you know so it actually does what I would like it to do But in the winters in new york, that's very rare winters in new york, it's going to be rare So maybe I need a different vehicle so uh, but you know do you have the tool set? You know we mentioned it as a tool before but Quantum is going to be one a very impressive tool in our toolkit, but it's part of our toolkit It's not it's not the only tool that we have it shouldn't be used as the only hammer and everything looks like a nail type of thing This is there's there's a number of other tools we have. How does it integrate? Well, how does it work to you know as kenny said you set up a problem? And you can find the right pieces of software or hardware Firmware to go after it and and it just works and you're not worried about But you know, what's funny now is when I give presentations on quantum, and I'm sure it's true for all of you Uh, you're immediately into some discussion about what kind of quantum is it as a trapped ion as a joseph injunction Is it something else which is interesting? I find that interesting, but that's not how we get to utility How we get to utility is how do you build that stack that people mentioned this morning? And how does that stack work effectively for you to solve problems device independent? All right, I think on that note I want to thank all of you for a really great discussion today. This has been excellent and Want to continue the conversation So the panelists have kindly offered to Continue the conversation with each of you if you would like to after the session They'll be around for the rest of the day today. So thank you very much