The New Quantum Era

The New Quantum Era, a podcast by Sebastian Hassinger.

And Kevin Rowney.

Welcome back to the new quantum era. Sebastian here with you. I'm flying solo. Kevin's taken a well deserved break. And, actually, during that break, we got a unique opportunity.

We got invited to the ribbon cutting of an IBM System 1 quantum computer that was being installed on the campus of Rensselaer Polytechnic Institute in Troy, New York. So I took that opportunity while Kevin was off. I attended the launch event. It was really spectacular. The system 1 is, housed in, the computing center, the Voorhees Computing Center at RPI, which is a converted chapel from the 19 thirties.

So the system 1 is actually sitting. It's a beautiful machine. It's an incredible industrial design. It's in, a a vitrine glass cube, with the dilution refrigerator suspended from, the seal of the the inside ceiling of that vitrine cube. And all of the control electronics are sort of hidden either in the casing or, or in another room.

So it's a beautiful sort of sculptural piece, and it's sitting in this room that has stained glass, that has, you know, sort of the the the the feeling of of a a place of high religion, and the the symbolism is is pretty apparent to anybody who's taking a look. It definitely was, it was quite a striking experience, quite a striking setting for it. And during that lunch event, I actually was, invited to host a panel. So this week's episode is going to be a recording of that live panel event where I spoke to a number of people from RPI who were involved in the decision to buy the System 1 from IBM and to work with them to deploy that on campus. It's the first functional quantum computer from a commercial vendor operating on a university campus in the world.

And so that's quite a a monumentous kind of historical event that I was invited to, which was really quite an honor. And I wanna thank RPI and the organizers for extending that invite. So today's episode will be that panel, and then, subsequently, we'll have a number of episodes because I was there for the full 3 days, and I was given access to the use of a podcast recording studio at RPI, which is, really luxurious. It was great to just be able to invite people in, sit down, and just start recording with professional microphones and a mixing tail mixing, console and the whole deal. So, the next few weeks will feature a number of my one on one interviews with attendees of the RPI launch event, and I think you'll enjoy the entire series.

There's a lot of really great guests, who were invited to speak that I've managed to get some time with in and around the the rest of the events of the week. And, again, this episode will be a panel that I led on, the Wednesday of, week before last, at RPI. So enjoy.

Now please join me in welcoming our host this evening, Sebastian Hessinger.

Alright. Hi, everybody. Welcome to the new Quantum Era. As John mentioned, I've been hosting a podcast, with my co host Kevin Roney for about a year and a half called The New Quantum Era, and I'm really thankful for the opportunity to join you tonight. This is, an historic event.

You know, rent Rentzillers is gonna be the 1st university that actually owns and operates a quantum computer on their own campus in the world as far as I know, and that's quite a momentous occasion in many ways. And so with me tonight to discuss that, we're gonna talk about, you know, the, the context of that decision, the the, some of the challenges, some of the opportunities that affords. So, please join me in welcoming actually, what I'll do, I'll introduce you each one at a time and you can do a little bit of a self introduction. So, Jim Hendler, professor and director of Future of Computing Institute at RPI. So, Jim?

Hi. I'm Jim Hendler, director of Okay.

I'm a faculty member here

at RPI both in computer science and cognitive science in our web science program, and I've been running some of our research into looking at originally AI innovation, now, overall computational innovation.

Thank you. Jackie Stampalia, director of client information services dotcio.

Yep. Hi. I'm Jackie Stampalia. I am the director of Client Information Services in our central IT organization, and I've been at RPI for 20 7 years. A lot of that time, I've spent in research computing.

And most recently, I have been very involved in the installation

scientist. Yeah. Yeah. Hi. I'm Osama Raisuddin.

I am a former PhD student at RPI, and I stayed on as a research scientist, because of the incoming quantum computer. And I'm interested in, using quantum computer for engineering applications.

Excellent. Thank you. And last but not least, Lucy Zhang. I can just barely see you all the way to the end of the day. Professor of mechanical aerospace and nuclear engineering here at the school.

Hi, everyone. I'm Lucy Zhang. I'm a professor in Mechanical Aerospace and Nuclear Engineering department. I am a engineer where users of all these computer, potential, new computers, my research on is on computer algorithm, developing computer methods to, solve engineering problems.

Awesome. Thank you very much. So, Jim, we'll start with you, the Institute For the the, you know, future computing. I assume you were involved in the early stage of the discussion to make this decision. What, in your mind, was sort of the the leading rationale behind wanting to, you know, have an on premise quantum computer as opposed to accessing it remotely?

So I,

those of you out on a podcast can't see me, but if you saw the gray beard and the the gray hair, you'd realize I've been around for a few years. And from day 1, you know, a lot of my interest in computing started when I went to the school that allowed us to actually touch the computer. And and seeing and interacting and learning not just to be sort of a remote user of something, but really to get an understanding of it. The other thing is, one of the things that attracted me to RPIF, right, in another university for over a couple decades, was very much RPI's philosophy of giving access to these things. So for example, with my previous school, if I wanted to use our supercomputer, I had to get a fairly large grant and pay a lot of money for the support of the machine.

Here, we actually and I get great credit to John and Jackie and some of the other people, have created something where that financing is somehow built into the whole magic of the thing that I don't understand. But, you know, my students are able to use it for free. I can use it with undergraduate, so we can do undergraduate research, graduate research. And so when the idea of the quantum computer came along, we really talked a lot about, you know, what was the future of computing? And, you know, sort of where did the innovation come from?

The innovation comes from students, both undergraduates and graduate students. And having the machine here, having the access, having the dedicated queues, etcetera. With that same, at least, you know, philosophy of we want you to use it. It's not here as some kind of beautiful thing to go look look at and watch other people use. It's get hands on, figure it out and make it do stuff.

And, you know, I think that was really to me the compelling argument.

Jackie, I mean, you were involved from the beginning as well. Was that sort of the thinking was was turning into sort of an unmetered resource or relatively unmetered resources?

Yeah. I mean, I think that one of the things is, and to follow on what Jim said too with our supercomputer, is just having having those resources here. I mean, seeing is really believing too.

And I think so much of what we do in computing today is in the Cloud and we're so far removed from it that it's really nice to have these systems here and having access to them, for for our whole community will is really a great thing.

Yeah. And I I imagine, I mean, as you said, you've got, quite a an impressive supercomputing, installation here as well. It goes back a long time. You've been in this in HPC kind of space for quite a while. Is there are there elements of sort of, sort of physical deployment, physical solution architectures that you think may be, able to be explored because these devices are both both sort of on premise?

Oh, Oh, yeah. Definitely. I mean, a lot of, structurally, environmentally, the cooling, the power, all those things. I mean, we have learned so much by I mean, our first system was installed in 2007, down in the at our supercomputer center. Having that knowledge, I think, really helped us even with this installation now and working very closely with the IBM team, which we did back in 2,007, and again in 2013, and

a chapel from the 19 thirties here?

So that was pretty challenging. Yeah. But it looks great. Yeah. It does.

It's really, it's really beautiful. But, so there were many challenges, I could point out, a lot of them, but one of them was, the quantum computer is installed in a vitrine with 10 by 10 glass and finding a way to get that in. And some people, I

think, have probably already heard this story,

but we had to to bring it in through some windows. Aye, because we didn't have a door that could handle handle bringing that in. So

I mean, the chapel wasn't designed for easy access to a quantum computer? I don't understand. It seems like terrible oversight.

Right. And I mean and for since 1980, it's had a data center in it. And even just having a traditional data center wasn't enough for the quantum computer. Right?

Yeah. They are exotic animals for sure. And, Usama, you were involved fairly early on in the in the install or in the early deployment of the computer as well. Right?

Yeah. I did, test out the quantum computer a bit, but, for Unofficially,

I heard. Unofficially, though. Yeah.

Yeah. But, as far as the installation and stuff is concerned, not directly. I was doing my PhD at that time. Right.

Right. Okay. And so, I mean, we talked a little bit about, you know, sort of the possibilities of of, you know, exploration of having this device on on-site. I guess, you know, Osama, your from your perspective, you're coming at this from, more the algorithms and the the, quant you know, software programming a quantum computer. Mhmm.

Are there ways in which you see that, being explored in the CS department that are, again, that are enabled in some specific way by by having it on on premise?

Yeah. Definitely. I think, not just on premise, even having access to the quantum computer. Normally, when people are doing studying quantum algorithms, they run on simulators. But when you actually start running on an actual device, it's like, you know, skipping a step going up or up or down the stairs, because, you're just hit with all these errors.

And once you actually start running on the device, you need to keep all of those things in mind. And, having access to those, aspects, experiencing them firsthand is very important, actually building real real world applications.

Right.

Right.

Yeah. And that sort of brings me to Lucy. I mean, in in your department, Lucy, I mean, you already deal with, as you said, writing software for simulation. Mhmm. Are there specific areas that you're interested in seeing how quantum computers may be able to to

help? Yeah. I mean, there are quite a lot. I mean, you even you look at all these engineering, industries today, looking at automotive industry, aerospace industry, or material science applications, they all need modeling. Applications, they all need modeling.

So it's very, very heavy. And if you go into these industries, you will see that they don't just build something out of scratch, out of thin air. They have to do predictive models first and then test it, iterate it, and then, potentially have everything, all pieces, worked out before they actually build the actual thing. So it's a very heavy component in the in engineering industry. So having powerful computers are very, very important.

That's what we've been doing all day long is trying to use the fastest computers out there to come up with algorithm so that we can do all these predictive models. I mean, nowadays, you look at, you know, people looking at digital twins of systems, and that's not possible without good computers. I mean, right now, people call little pieces as digital twins, but there's it's the it's the concept is there, but in reality, it's so difficult to implement without a good hardware and software in the background.

Interesting. Yeah. And, Jim, like, you've been involved, as you said, in technology development for a very long time. I think you mentioned DARPA to me, in fact. And I just wonder what you think about the, sort of, the role of, collaboration between academia and industry and the public sector sources of funding to drive this sort of stage of of technology development?

Right. So there's a somewhat pervasive myth that industry does applications, universities do curiosity driven science and sort of throw that over the wall to industry to figure

it out.

But when you actually look at the history of a lot of things, I don't care whether you're talking airplanes or you're you're talking the Brooklyn Bridge or you're talking, the quantum computer. You're really looking at something that needs these partnerships. You really need somebody who's saying, look, I realize it won't yet solve this business problem. But if we someday want something that can, we have to start now in that direction. And that's traditionally been the But now the question becomes, what directions for what problems?

Right? We're facing as a as a as a as a species, these global challenges, climate change and fresh water, you know, we'd go through lots of others. But if we can't learn how to do something with those and do it in a way that transitions to applicability. So we can have the greatest algorithm in the world for predicting something if no one can turn that into something that actually becomes practice or practical, that's a problem. In an engineering school, you do have a lot of that knowledge about how to build things, but you still need the the science and curiosity at the beginning.

You need the interaction. So I think our relationship with IBM was actually, some years ago, I wrote a joint article with someone for the, proceedings of the National Academy of Sciences Magazine on university industry partnership. Some of what you have to do to get it right, but most importantly, why it leads to good science.

Mhmm.

It leads to science that's meaningful. It leads to doing stuff that you'll forgive me for for quoting the RPI model, but it's the thing that lets us change the world.

And, Jackie, the the way that the supercomputing, facility has been utilized, does that sort of bring that kind of industry academia collaboration into play?

Oh, yeah. Definitely. I mean, the RPI researchers that we've had using it have brought in industrial relationships they've had. So, working with industry partners, they've made use of the systems. Right.

Right.

And that's I mean, I assume that'll extend to the quantum device

as well.

The plan is for that to happen.

I guess that I mean, that one of the huge challenges, I think, with quantum computing in general, and I'd love to get your view on this, Osooma, like, it part of the problem is that it's operating on under a completely different computing paradigm that has a lot more to do with physics than classical computing does. And it's really difficult to get, business leaders and technologists in industry

how to make that happen or a perspective on that? I mean, a lot how

to how to make that happen or a perspective on that?

I mean, a lot of work has been done in, developing algorithms, but I think the first advantage that we might get or the first use case is definitely going to be using quantum computers to simulate quantum mechanics. And that doesn't mean that it's it's just some physics problem. This is something that is relevant in engineering, and I keep bringing up this example of, like, multi scale modeling. So that is something where, you try to simulate the, properties of materials, and then you start sending that information to, like, larger scales to simulate maybe even like a like an airplane or something. Right?

Right. So, I think those use cases might be, coming very soon.

Right. Lucy, does that resonate with sort of the challenges that you have? I mean, often, large scale simulation is is doing it in approximations. You're making guesses along the way as to what is the the relevant data and what data you can afford to throw out. Mhmm.

Is that sort of where you see potentially quantum computing being able to provide some more precision in areas that that need it?

Yeah. Absolutely. I mean, it's not just, you know, large, quantities of parameter that we have to look at. It's also, you know, there are uncertainties in these parameters as well. So that opens up, you know, whole new space that we haven't got a chance to explore yet.

And right now, we're just do very, you know, deterministic. A lot of them are very deterministic based on partial differential equation with some boundary, you know, fixed boundary conditions, and we get a set of solutions. But it's in reality, the nature doesn't doesn't end just there. So, yeah, totally. It needs a lot more.

And I guess also, I mean, I I imagine aerospace is often problems that have, at their core, material science problems like corrosion and stresses and and at that level, you are dealing with a quantum system at the at the material level. It's really interesting. No.

Oh, go ahead.

Yeah. So yeah. So certainly materials and material, you know, physics behaviors like erosion, for example, for sure, and also, you know, material discoveries. A lot of that needs random collections of, you know, parameter spaces, and how do you put that together randomly without, you know, that we can get into. And like I mentioned, like, right now, you do a crash test.

Right? So you build a car. You design a new car. You have to do those crash tests. You don't just crash a real car first.

You do all these simulations first, and there are a lot of, you know, uncertainty probability in in those, physics, problems as well. So a lot of spaces.

Yeah. And, Jim, you and I were talking earlier. This seems like a good opportunity to bring in the unholy marriage of quantum and AI, which everyone wants to tie together. And there is some sort of relationship there, but it's very complex. But but that multilevel kind of approach that ties together machine learning, for a large problem space, and then, simulation is is really potentially powerful.

Right?

Yes. So we were talking about a model that I'm very excited by. So, let me let me take one example. Right now, protein folds is a important piece of the biomedical story, but it's not the whole story. The the real story is how that folded protein binds to various things with various functions, which happens at a quantum level.

So what happens is you take a protein description, you run it through AlphaFold, you get a few good examples. You can now go to the quantum machine and and really model at the quantum level what's going on and say, okay. Here's one that looks like it really works. Now, you're still in the probability space, so you've got some good probabilities from the AI, some really good probabilities from the quantum model. You now go over to your traditional modeling you know, you know, just because I say, hey, the quantum computer said it might be a good idea.

So you're looking at sort of something that takes a very large set of data and turns it down to a few probabilities. Something that takes those systems and puts them together at a very deep level. And then you can go to something that can really say, okay. Now we go to the kind of thing Lucy's been talking about and say, let's really see, you know, does this make sense? But but the thing is, if you try to use any one of those technologies alone to do this problem, you know, the AI won't give you that precision.

The the the supercomputing can't run through 200,000,000 proteins to find you the best faults. The quantum computer can do the quantum level analysis but it really, you know, isn't the right machine to do sort of the the high level, you know, when I hit a table, my hand doesn't go through it. Right? But theoretically, in quantum space, it could. I mean, it's just the probabilities are so low they don't happen.

But when you're talking about a protein binding these things together in new and imaginative ways. And I think the marriage of these different sort of heterogeneous and composable systems is gonna be absolutely crucial to the future of computing.

And Jackie, that that sounds like a a problem statement, a solution architecture that's tailor made for for what you have on campus now. You've got all those those components. Are are have you started thinking about how those HPC classical compute resources will be integrated with the quantum computer?

Well, yeah, we did today. Right, Osama? So one of the workshops we had today was, quantum HPC integration.

I might have looked at the list of

4 facts.

Thanks for that.

Little bit of a cheat.

So so yeah. We we are starting we are starting to look at that. I think it's gonna be very exciting working with IBM to to do that, to get that set up.

That's great. And, Osama, coming back to, sort of, the algorithm perspective, so do you sort of the algorithms you were interested in, are they primarily, sort of in that Feynman way of thinking of quantum computers where it's it's you know, if you wanna simulate a quantum system, you need a quantum device to do it?

Right. So from a very, like, practical perspective, yes. You that would be, like, the first early application. But I think from a computer science's back perspective, getting to know what the complexity of solving a problem is, I think, that's very interesting. And and that's actually where I started my research, like, in during my PhD research.

I was more interested in the complexity of solving, like, linear systems. We can get some exponential speed ups for some particular cases.

That's great. So so then those are more like, those are algorithmic primitives are more generally applicable then. Is that is that fair to say?

No. Not generally applicable.

Well, applicable in multiple types of Right.

Level and then Given that you satisfy their, conditions that, for example, the condition number of the linear systems scales well or something like that.

Yeah. And, I mean, there's sort of it feels like listening to Lucy, there's kind of a high level sort of industrial application perspective. The sooner Jackie, there's sort of an operational perspective. When I when I look at the algorithm research that's going on, it still feels very, low level. It feels like closer to assembly language Mhmm.

Than the high level languages we're used to in classical computing. How do you see sort of bridging that gap between where the computer science is with quantum computing right now to where it needs to get to for these sort of large scale end to end solutions?

Yeah. I think, it's it's a long way to go, I feel like, and and for sure there have been quite a lot of, theoretical foundations that has been built, I don't know, 10 years ago, even 15 years ago, about how to solve a linear system, like a linear algebra, right? So you, that's potentially what a lot of engineering physics are governed governing equations are. We have a lot of nonlinear equations to start with, but we try to linearize it. I think, the, you know, the easiest or the fastest way to think about this is is that how about these, you know, complex equations or large size equations, you know, like Osama was mentioning about, you know, multiphysics interactions and and all of that, you can still start by boiling down to linear equations.

You linearize it first. And then I think we finally have hope because all those theoretical foundations that people have been developing for linear functions and, you know, linear equations, we can finally test it.

Mhmm.

We can test it. We have a way to explore it now. And once we have that all taken care of, then we can start getting into more complex systems, you know, like all the stuff that we want to do. And in fact, 30, 40 years ago, that's how all the mechanics people started this. Right?

So, you know, you you look at all these industries. They didn't have anything, like, compute computer algorithms before, and then they started with theoretical math, you know, in the background first, and then when the computer scientists figured out the software, the language, and all that put together, we're like, okay, finally, we can finally test out our theoretical foundations that we've been building.

Right.

And, so then eventually 30 years, you know, passed, and then we said, okay. Now it's finally mature and becoming products. And, now it's becoming a standard for all of these, design or, you know, applications. So I think we're getting there. So I think we're, as as potential users, we're always trying to we're we're trying to adapt, and we have foundations there, and we're keep on adapting.

Yeah. That that collaboration around innovation sounds very much like what you were describing, Tim. I mean, that's what do you see at this stage? Given you've seen technology cycles in your career, what what do you think is most important to focus on at this stage?

Yeah. So I actually think that we've touched on a lot of these things, but that computer level that that move up from assembly language to something I think is very important. 1 of one of the people who will be here tomorrow is one of our rising stars, Andrew Young from MIT, made a great point in a talk he gave here a few months ago, which is that, you know, there's a lot of ways to make errors in quantum computing that cause the thing to sort of collapse. And if you make one of those errors, the way he described it is you end up with a very slow, very expensive computer. Right?

Which is not what we're after, obviously. So so really learning how to build the some of the software things at a level that's quantum safe, but it's still at a conceptual level. The chemist doesn't wanna learn physics. The material scientist doesn't wanna learn, you know, quantum gates. You know, what made life change was first Fortran and then higher and higher level languages.

So, you know, my students are now doing things in neural nets that I come to imagine 20 or 30 years ago. Like, writing 4 lines of Python code. Right? Which compiles down into tens of thousands of of machine instructions. Right?

We're not there in quantum and we're gonna need to learn to get there as well as how to have some of these things interact so that if my AI system says here's a good potential, candidate for something, how do you represent that over to the the quantum machine?

Wanna solve real world problems with

these machines, need a tool, wanna solve real world problems with these machines need a toolkit. And, you know, one of the best ways I've seen over the years of getting those toolkits built is by solving real problems. Okay. How did we do it? What could we reuse?

Etcetera, etcetera. And I I expect over the next few years, we're gonna see a lot of that same thing happening in the quantum space here where our students were are using existing, things like, you know, this is really great, but, you know, if we added this or if we change that or if we could embed it in this, then it becomes much easier. And then the next person comes along and takes and builds on top of that. And I think it it's, you know, we've seen 50, 60 years of that continually happening in the traditional computing space. And now quantum is coming into the picture.

And we need to figure out, you know, sort of how we bring that into the family, as opposed to becoming a dysfunctional family going off in different directions.

And hopefully less than 50 or 60 years.

And hopefully less than well, let's just say it's been a very accelerating curve.

Yes.

And, you know, we always look at the graphs now on exponential charts, so it looks like the straight line Yeah. Because otherwise it just

Yeah. Yeah.

Yeah. Shoots up so far you can't even see.

So, Sanwat, your PhD was focused on more complexity and and the theoretical side of things. Do you see, now that you have access to a real quantum computer, is there are you using a path towards making you know, tackling more real world problems with that that kind of background?

Yeah. So, the problems that I was working on, hopefully, once we have, like, error corrected algorithm, computers, on those, we could potentially run those kinds of algorithms. But I think we have a long way to go, in terms of that. There's a clear road map, though. And and I think it's gonna happen in the next decade or so, if not earlier.

Yeah. Yeah. Yeah. Certainly. I mean, you know, IBM's paper saying that they can do 12 oh, sorry.

Yeah. Twelve logical cubits out of 288 physical cubits. That was Yeah. Pretty astounding. So, I mean, there are definitely logical cubits error corrected quantum computers are on the way.

Is what what's the main sort of task now before we have those those error corrected cubes? Is it is it still theoretical and and, and sort of trying these things out on on physical cubits? Or

I think, right now, the main task to prepare for error corrected hardware would be to start building up the software stack and, make things more modular. So, for for example, there's this famous algorithm called the VQE, variational quantum eigen solver. But at at its core, like, it's just an eigenvalue solver. Right? And this is a particular problem which is, solved not just in physics, but in engineering and, in a lot of different fields.

So I think, there is also this, aspect of being able to learn the language and cross bridges, because, people hear quantum and they can kinda get spooked from

Right.

His. So I think, that bridge also needs to be cross to for applications to come in.

So do you see sort of co design opportunity with Lucy and other other professors in other schools of engineering sciences?

Yeah. Definitely. A lot of these, fundamental, problems, like QAOA, VQE, it's like a eigenvalue problem, combinatorial problem, linear system problem. They show up all in all of these different, areas of study, and, there's definitely a potential for that.

And, Lucy, is there anything I mean, do you think you're gonna need to, like, Maine will have to introduce more QIS sort of curriculum in order to sort of meet the, the theorist halfway at this stage?

Yeah. Absolutely. I I just sent a student to Osama.

You you also are getting a new course, right, in the next semester?

Perhaps. I don't know about. Yeah. Likely. So, I'm currently at the the National Science Foundation as a program director.

So I see a lot of, workforce development type of calls now that, you know, people wanna build build it up, because potentially, that's a workforce that will that would be missing if we don't act now.

Mhmm.

So, certainly, there are so many opportunities. And for the, you know, even for the current undergraduate level. I I tell my son the other day. I said, did you know that we're gonna have a quantum computers? He was so excited.

Right? So I think I think that next generation, they are they they are our hopes. Right? So they're interested, and then I think by the time they grow up, or even our current undergraduate graduate students, they have to be somewhat informed, kind of go along with the, you know, whatever, just be informed in some way. So because the market the workforce market will be missing those talents.

So for sure, you know, curriculum, you know, development and definitely, you know, some type of training, either whatever it is, you know, could be on the side, could be, you know, just purely out of in interests. We can build that up, and then we have a we have a computer in hand that we could do that.

Yeah. And workforce development is a you know, the National Quantum Initiative mentions it, quite a bit in that that bill or or that law, and obviously, NSF has enormous amount of resources dedicated to that. I I wonder, and this is an open question for anybody, what do you think, RPI will be able to accomplish in terms of curriculum development with with the unique resource of having an actual device on campus, which is is, you know, uniquely, a unique situation to be in.

So let me jump in and, well, I absolutely think there'll be a lot of things happening in our curriculum. Frankly, I think the most exciting thing will be what's happening outside of our curriculum in project spaces. And, a lot of our classes, the last thing you do in the term is a group of students works together on a project. And if you've listened to the sort of applications we're talking about in some of these different levels, I think for the foreseeable future, future, if you're gonna use these technologies, you're gonna need someone who really understands the problem, someone who really understands the quantum computing aspects, some other people who may know some of the software techniques or, you know, the mathematical equations. Right?

And, you know, sort of in the early days of computing, it was very much like that. You couldn't you would you would sit and you'd write the program by yourself, but then you'd have to go find someone who actually understood what it was supposed to do to check that it was actually doing it. And and I think we need a lot more of that kind of interaction on a much deeper level. So I think a lot of our curriculum is going to include both learning the depth of this is the math, this is the physics, this is the programming style, but also those cross cuts. Let's look at this problem.

Right? And if you've looked at at the posters, there were some really nice ones. They from some students who in a project course got some data from NASA decided to try analyzing it using various learning techniques including, the quantum learning techniques which they ran on the simulator And sort of by next week, they'll be running it on the real computer and be able to get us actual, you know, these are the comparisons between this kind of network, this kind of simulator, this kind of actual machine. And then we can watch those things change over time. And it, again, it took a bunch of students, some who understood the problem, some who understood the computing.

And so I think we're gonna see both in courses, but especially outside of courses, this kind of teaming and doing it. And here I mean undergrads, grads, faculty. I mean, these cross, campus teams are just really gonna have to we're gonna have to find a way to enable that, make that work. We we have a lot of those mechanisms already in place with our PI. It's one of the things that makes us special.

And I think the quantum machine is gonna fit very neatly into that paradigm of students proposing things, people being able to advise on that. Those things becoming the curriculum. So I actually see almost the curriculum as a as a, what what's what's the word beneficial spiral? The Mhmm. There's a term for it that I'm blanking on, but where each time around, it gets a little better.

So I think that's what we're gonna see happening here is the courses will enable the students to do things that will enable new courses to be designed, that will enable next set of students to do even better things, and I think that'll go very quickly.

It's interesting. And I guess this is for everyone again. What, to what degrees, because, you know, every time you look at the challenges, the progress, we've been talking mostly about applications and sort of, finding these commercial or productive uses for quantum computers in industry. There's also the whole topic of how to make quantum computers work better. Right?

Error mitigation, error correction, you mentioned this, Salma, the the the algorithmic primitives themselves. What, you know, what's the balance at RPI in terms of focus? Is it 5050 in terms of trying to improve the field versus trying to find applications to the field? Or what what how do you see that that split? Sure.

Anybody.

Yeah. I think both sides need to be tackled at the same time. And I think, recently, like, we've been surprised by the amount of progress that is being made. I I think just this past week, IBM came up with a new, code for error correction, which requires, like, orders of magnitude fewer cubits. So I think, putting efforts into both ends, into making, the hardware better and also trying to improve the goal faster.

And, Jackie, is there I mean, in the supercomputing center, is there sort of a tradition of sort of feeding, you know, that that kind of fundamental improvement back into the into the the the vendors, into the the

At the hardware. Technology?

Yeah. Definitely. There is. We have a partnership with AI, which, Jim, the AI Hardware Center, which Jim maybe could speak to

a little bit. So so for a long time, we've had an AI relationship. Well, we've had relationships with IBM for a long time. For the past few years, we've had a very heavy one in AI that's now expanding to AI, AI hardware, microelectronics, and quantum. And the idea of these four tracks is not to be 4 parallel things that don't talk to each other

Mhmm.

But to really encourage this kind of interaction. Jackie's role in this is making it possible for us to do it. My my role

is convincing

the is convincing the students that they should be doing it. But more seriously, it really is the case that there's gonna be a lot of exploration going on and I think this is a great place for it. And what we've done as our supercomputer has evolved from where thanks to NVIDIA and some of our partners and, you know, we'll hear from some people tomorrow who are very involved in this. Hi, Curtis. That that, you know, now our supercomputer is essentially an AI enhanced supercomputer, so it can be used for standard linear supercomputing and some of these other modeling simulation techniques, but also has tons of GPUs which we can use now for these AI learning techniques.

And so we've been able to rather than having to go off into a cloud, rather than having to depend on other people's software are able to do our own experimentation and then working with IBM who understand some of the internals and have been able to improve some of that. Some of the other things we've done together. So in the past 5 years, we've had well over a 100 joint publications in top conferences and journals, we have RPIPI RPI and IBM authors together. And I think in quantum, we're already having the discussions about how we're gonna make that keep happening, how we're gonna grow that, how we're gonna really create this partnership as a partnership.

One of the other things we talked about today too was, classical computing and how to integrate then quantum computing into different parts to to further along what what the research is. And that was an interesting conversation, I thought.

Thought. Yeah.

I think even to do quantum computing, like, you have to come up with the circuits, you have to make them efficient, you have to convert your circuit into the, laser pulses or microwave pulses, etcetera. Then, for that also, to make it efficient, you might need to, high heavy, classical computing resources. Right. So that's an interesting, integration.

Yeah. That's the other quantum AI meaning that I I referred to earlier.

Yeah.

I mean, that that's I mean, that feels like in order to when we have some amount of quantum advantage from a quantum computer, we're gonna need to have very tight integration with the classical computing resources in order to not lose whatever

Yeah.

Going with that. So is that just talking

about today.

Faster network interconnects, or is it, like, actual physical collocation or rack? Or, I mean, how are you thinking about that?

Yeah. I think, faster network interconnects, definitely. Also, for if you have, like, parameterized quantum circuits, then, running those circuits on the fly, one after the other. So that's more, classical computing

Interesting. Interesting. And so, like, if you're if you're trying to make this available for that kind of very open ended experimentation and exploration, Jackie, like, how do you manage a resource like that? You've got one quantum computer. There's probably gonna be more than one person wanting to use it.

Yes. Is it back to time share days? Is that what we're doing? Or

Yeah. Well, we did talk about our so we

use the SLURM scheduling if

you're familiar with that. Yeah. I it it reminds me of something many years ago when when we were working with IBM, the one of the folks at IBM, he was an IBM fellow, who I would work with periodically to get people, because IBM was using our supercomputer too. And he said to me, you know, the best you can do is always keep everybody a little unhappy. You don't want anybody too happy because then everybody else will be very, very So so hope I think that might be our goal.

Distributed annoyance. Distributed

at a very low level.

Yeah. It's I mean, it really is a unique I I have not seen this level of sort of open ended exploration with a device by an academic institution, and that's really exciting. I mean, it's it's it feels like, you know, we don't know what we don't know about quantum computing, and it's this kind of you actually said this to me in one of the earlier conversations we had, Jim. You said, if we don't explore in this way, it won't happen. Right?

There's sort of this open ended exploration that's required in order to have that moment of serendipity where the the the the insight happens. Right?

You you know, I take out my my telephone and open chat GPT and, you know, play with it. Right?

That's what you meant by having a relationship with AI.

Is that right? 30 years ago or more, actually, oh, god More. You know, we were doing I mean, it was a PhD thesis to to do this story. Jack went into the restaurant. He ordered a lobster.

He ate and left. And get the student to answer the quest get get the computer to answer the question, what did John eat? Right? I actually was the programming assistant to one of our PhDs who was working on virtually that exact problem. Today, while while we were chatting, I actually asked ChatGPT, he thought that ChatGPT could say,

hey, lobster.

Lobster. So so we've evolved this thing, and it seems like it magically happened overnight. But in fact, if you really trace the stuff back, you see different innovations. You see different kind of things. And I think in the, I've been at RPI now 17 years, but I've been really working in this particular research space of trying to put these things together for the past 8 or 10.

Just the acceleration of which we've been getting and and the possibility, again, the joint work with other with IBM and other partners has really made this stuff accelerate. And a lot of that is because we can really try things. And that trial and trial and error are crucial because if we're going to ever figure out how to really use these machines right, we're going to have to make mistakes. And we have to be somewhere where that can be tolerated. And again, you know, having the machine on I don't mean we're gonna make mistakes that cause it to blow up.

I mean, we're make machines make make mistakes that cause the quantum equation to collapse. And suddenly, you know, we're gonna look at the computation and say, oops, maybe we better change that code or think rethink how we're doing that. I think that's where the real learning is gonna happen, and that's really exciting.

And, Lucy, like, if you imagine that kind of enormous progress over the next, say, 10, 20 years, and to the point where we have, at scale, we have sort of a quantum mechanics lab in a computer that's totally programmable. You can just program any quantum system and watch the evolution, see its attributes. What's sort of, like, the dream scenario from an engineering perspective for you? What would you be able to do if you could do that?

I think, you know, I mentioned digital twins earlier. I feel like that's the ultimate system level, you know, capability simulation that that we as an engineer, I would like to the computer to replicate in real time what the solution should look like

Right.

For a whole system, not just parts of it Right. And not waiting for 2 weeks or 2 months for, you know, some results. I think for us, that would be a dream, and certainly, you know, a lot of, things need to be in place beforehand.

Well, I'm just reflecting on that that level of advancement that Jim was talking about. So, Osama, same question to you. If you imagine that

sort of giant evolutionary leap over

10, 20 years, what do you think will be quantum physics in a box?

Yeah. It's interesting when you say quantum physics in a box. I have a background in fluid mechanics, and I kind of look at quantum computers as, like, a wind tunnel experiment, but for quantum physics. I think, something very interesting would be to simulate large scale materials, like the way so there are these, some phenomena that we cannot recreate in our simulations. For example, if you have a titanium bar, right, and if you run elect an electric current through it, it suddenly becomes soft, and you can machine it much easier.

So if you can explore that kind of physics, like the excited state physics, I think that would be very interesting. And you could come up with new manufacturing processes, like you excite your material in the right way, you shine the right wavelength onto the material, and suddenly it's, much easier to machine. So that would be like a dream scenario.

Right. Almost like programmable materials in a sense. It sort of makes me think of, you know, moonshots are it's a metaphor we use a lot for sort of these grand kind of scientific and technological missions, and it's a reference to the space program which, you know, there is no direct sort of benefit from landing on the moon commercially, but we learned so much in investing all that time and brainpower that there were tons and tons and tons of sort of secondary and tertiary economic benefits and and commercial benefits. Is that, do you, is that kind of how you imagine, like, again, this sort of material foundry in a in a in, you know, simulation in a box, you could imagine, like, so many different, like, offshoots from that that would be valuable?

Yeah. Definitely. I think, the same kind of, discoveries could be applicable in other domains like fluids, perhaps. It's possible. Excellent.

You know, some years ago, one of our honorary speakers was, Craig Munday of Microsoft at the time. And, at a dinner with him, Craig said, you know, we really don't have an energy problem in in our country. We could deploy enough solar to to power loss edge we get as things run through the wires and the cables and that.

And he said, you know, if we could solve sort of room temperature supercomputing as it were, or at least reason for temperature, supercomputing should only

have Superconducting.

Supercomputing. Yeah. I'm sorry. Superconducting. Yeah.

I do

that all the time.

I sit next to Jackie. I'm on a microphone to say superconducting. But the but really, you know, in a sense, these things can really be world changing technologies. And, you know, when you talk about running the electricity through a titanium bar, we know some things happening in there. Once we can start to figure out what's happening in there Right.

That opens up the possibility of of really looking at these lower level technologies as well as these high level problems. And then saying, hey, this gives us the tool we need to go after this next step and next step. So again, I I you know, you keep saying 10 or 20 years. Frankly, I'm hoping it it's 5 to 10, you know, maybe less because I think this stuff is gonna

move very quickly. Yeah. I agree. I mean, you can see

the beginnings of these advances in the applications now. It's just, you know, the time horizon extrapolation can help sort of crystallize some of these things, because it's hard to see them when they're right in front of us sometimes. So, Jackie, like, do you, you know, do you foresee like, if there are that many sort of applications and uses for this thing, do you get a second quantum computer? Is there

That's the question for me.

Raise your

checkbook. We'll take another one,

Trevor. Yeah. I mean, I think

I guess my question is, does that quantum data center, does that expand over time? Is that is that sort of

Yeah. I mean, I I'd like to say that our we have in in the time that I have been here, certainly, and goes way back far farther than that, our computational resources and capabilities, RPI, with generous gifts and other things have always carried on. Right? So I don't think we're done by any means. I I don't wanna think we're done by any means.

Wow. Excellent.

Any last thoughts from anybody on the panel? This has been really interesting, but I wanna make

sure if there's any. Osama, it looks like you've

got one. Excellent. Yeah. So, like, quick story. So, during my undergrad, I got my new laptop for my, college degree.

Right? So during my undergrad, I had a laptop with a GPU, and back then, like, not every laptop had a GPU. And because I did have the GPU on my laptop, then I wanted to run my codes on the GPU. And I tried to do that with MATLAB, so, it does I didn't get very far. But the same thing is with the economy period.

Like, now that we have it on campus, it's available to us. Now there's the itch to actually, like, use it and run things on it. So I think having, access, and being close to the computer itself, I think it does drive people to use it a lot more.

Yeah. I think there's an undeniable, even psychological effect of just that machine sitting there in that chapel, in that beautiful setting, it's it makes it top of mind, it makes the imagination sort of start to run wild. So, well thank you very much everyone on the panel. Thank you very much everybody for being here, and that's a wrap.

Thank you.

Okay. That's it for this episode of The New Quantum Era, a podcast by Sebastian Hassinger and Kevin Roney. Our cool theme music was composed and played by Omar Costa Hamido. Production work is done by our wonderful team over at Podfi. If you are at all like us and enjoy this rich, deep, and interesting topic, please subscribe to our podcast on whichever platform you may stream from.

And even consider, if you like what you've heard today, reviewing us on iTunes, and or mentioning us on your preferred social media platforms. We're just trying to get the word out on this fascinating topic and would really appreciate your help spreading the word and building community. Thank you so much for your time.