The Deep View: Conversations

Ian Krietzberg:
Hey everybody, welcome back to the Deep View Conversations. I'm your host, Ian Kreitzberg, and today I've got a really fun episode for you. My guest is Jerry Chow. Now, Jerry is an experimental physicist, an IBM Fellow, and the Director of Quantum Infrastructure at IBM. And if you hadn't guessed it already, today we are taking a deep dive into quantum. Jerry, thanks so much for joining us today. Good to be here. Cool. So there's so much to get into. I'm so excited to chat with you. Um, I, I was at, uh, the quantum computing facility in Yorktown a couple of weeks ago, and I'm very interested in diving into quantum, um, more deeply, but I, for starters, you've been studying quantum for a while. Um, and I guess I'm wondering, you know, for you, why this technology is worth, you know, dedicating your life. Towards, you know, expanding.

Jerry Chow :
Yeah. So, I mean, like, my personal journey is related to, I guess, an interest in physics at a pretty young age, right? So, like, my father is a physicist and, you know, he's a retired professor of physics. And I'd always been, you know, interested in challenging topics. Right. And but I was always interested in physics from the point of view of something that could be actually applied. And when I went into my studies in university and then subsequently in graduate school, I think quantum physics certainly had that difficult element to it, but challenging element to it. But then what was really intriguing to me was that it also had this potential practical element to it in terms of studying the quantum mechanics within specific devices. And this leads down the path of exploring quantum mechanical phenomenon to build qubits. And lo and behold, that technology can actually build towards a completely novel and new computational platform. And so, you know, that's sort of how I got my feet wet into this area, which was really, you know, from a experimental applied physics angle, right? But that's really, you know, taken a life of its own in terms of becoming a technology that you know, lays this groundwork for a really novel future of computing path.

Ian Krietzberg:
And so you mentioned qubits. And just, you know, for starters, I know it's broad, and I'm sure you probably get asked this question a lot. But, you know, when we think about quantum, it has a name, right? That seems kind of inaccessible. But what exactly is quantum? And how does it compare to the kind of computation that we've become so familiar with?

Jerry Chow :
Yeah, I mean, I think, you know, quantum sounds scary, right? But in the end, it's just a branch of physics. From the point of view of information, right, it means that there are a different set of physical laws, right, that information is governed by in the quantum computing realm, right, than traditional information. And that's really the core difference, right? That when you have underlying different math, there's different ways of computing and different things that you can do, right? Traditional computers, we talk about bits, zeros and ones, and physical instantiations of that are voltage levels within a transistor and things like that, right? or physical switches and computation that all we know with our laptops, our phones and everything that we know in a lot of our modern technology is based off of computation, based off of the progression of those types of Boolean logic. But with quantum computing, we have quantum bits and quantum bits become now the information carriers. They follow these different laws of quantum mechanics, which gives them special abilities, and in essence, allows us to compute quite differently, and in certain cases, compute certain problems in a way that you can get sort of an exponential increase in capability than you would with classical Boolean-based logic.

Ian Krietzberg:
So you mentioned bits and qubits, right? And bits are the zeros and ones. From my minimal understanding, qubits are able to be zeros or ones? They can kind of interchange or something? Like what exactly is going on? I know it's a big thing, but.

Jerry Chow :
In the quantum information realm right now, you have Azure logical unit of information that is the quantum bit or qubit. And you can think of it as they can be in in different states and they can be in a zero state or they can be in a one state. But they can also be in superposition states, right? Where there's a some there's they can be in, you know, some people say a little bit of zero, a little bit of one at the same time. But the fundamental thing there about the laws of quantum mechanics is that when you actually need to ask that bit, that qubit, you know, what state is it in, you have to actually perform a measurement. And so you force it into either one or zero. And so that means that there's always still a deterministic result at the end. That's a 1 or 0. But at some given time during a computation, it could be in some kind of superposition of 0 and 1 at the same time.

Ian Krietzberg:
And that's the critical element that makes it more robust or different. One of them, right.

Jerry Chow :
One of them in the sense that it allows you to, during the operation of a computation, and oftentimes we call the the unit of, not information, but the unit of computation, like sort of a quantum circuit. A quantum circuit is a collection of operations on various qubits. That when you perform operations on these qubits, these information-carrying elements, that they can be in things like superpositions, they can also be in things like entanglement, entangled states, where there's actually where there's actually this capability where you can't actually extricate information into individual pieces, that they can only be seen in a collective way with other qubits. But the cool thing is that all that happens within the context of inside of a computation, inside of a circuit. And at the end of it, you always still measure. and you always still get back zeros and ones. So you always still get a deterministic result at the end. And that's what actually allows us to then further compute using those results, combine it with classical logic and so forth.

Ian Krietzberg:
So, and I believe IBM put its first quantum computer up in 2016. So we're looking at almost 10 years now, and I'm wondering, you know, we've seen a lot of advances in sort of technological capabilities, looking at artificial intelligence for one, right, in the past couple of years. I'm wondering over that timeline from 2016 to now, what has changed in quantum? Are we getting closer to a place of, you know, usability?

Jerry Chow :
No, absolutely. I mean, there's a lot that's been happening in the sense of, certainly from our perspective at IBM, that initial project to place something on the cloud really showed that we could turn this from something which is like a physics experiment into something that can be a computational tool, programmable, something that can have an underlying instruction set And from that, the early days, really trying to build up the community, build up interest in learning about this new form of computing. But since then, we've really matured in terms of how we see it being a part of the full computational ecosystem. So that, you know, quantum computers don't exist in a vacuum, they exist within the context of other, other more traditional computing resources, right? But they happen to be very good at solving specific types of problems. And, you know, we can think of like solving any type of problem as like, how do you represent that problem? Or how do you break down that problem in terms of some kind of effective data representation? And If it happens that you can represent that data very well, the problem very well, and the data very well in like, you know, a Boolean-based logic, then you're going to use traditional computers, right? If you happen to actually represent it very well in the language of matrices or linear algebra and tensors, you might use GPUs, right? But now there's a different set of problems that you might actually want to map onto this language of quantum circuits, right, where we are leveraging these, you know, different features of superposition and entanglement and, you know, quantum measurement, different set of language, different data representation, and a different set of tools for solving problems. So it's not meant to sort of replace all your computational platforms, right, but it coexists with these other ones. in a way that you can actually tackle much more challenging problems but that make use of these various different types of representations. So the evolution of how we think about how it fits within this overall computational architecture is one thing. But also in the same period of nine years, eight years, we've progressively improved the capability of the underlying hardware and software. From scaling up the number of qubits, but also improving the quality of the execution of these quantum circuits to get reliable results.

Ian Krietzberg:
I'm glad you mentioned that because something that I've been kind of wondering about in the AI world, there's a lot of benchmarks. You know, we think a lot about benchmark testing. There's all these different ones. I think they put out a new one recently. When it comes to quantum, you just kind of mentioned scaling up qubits and increasing kind of reliability. I wonder. How do you measure the efficacy and utility of these things? Is it a similarly benchmark-focused thing? Like, how do you assess that?

Jerry Chow :
No, certainly. I think it's an area of... It's certainly an area that we've been focusing on to show that there's improvements in the underlying quality, right? And it's just not just a number of qubits for that matter, right? One key thing that we'd like to point to is the, is in essence sort of the, you can think about it like the complexity of what you can run on a quantum computer is governed by the number of qubits you have and then the number of operations or the number of gates that you can effectively run and still get useful information or you get meaningful information out that's not corrupted by, say, the noise, inherent noise in the physical architecture of the system that you have. And so a good rubric really is, you know, you have the size of this circuit that you can run. And so there's both the number of qubits you have and also the depth or the number of gate operations that you can run and still get accurate results. This past year with our Heron processor, we actually showed the ability to push this all the way out to 5,000 gates. So 5,000 gates of meaningful execution of circuits across 100 plus qubits. And this is a number we want to continue to scale out, right? That year after year, as we further improve the capabilities of the underlying hardware and quality of the hardware that we had, that we can continue to scale out the number of gates. And ultimately, this is also where things like quantum error correction come in, to really increase the size of the circuits that we can run that leverage error correction as well. Today we're using techniques known as error mitigation, which gets us to this point of 5,000, eventually several thousand, 10,000, 15,000 gates. But to push into this realm of several millions of gates, or even hundreds of millions of gates, we're going to use quantum error correction.

Ian Krietzberg:
And as we're in this environment where you're scaling up, you're working on increasing the quality of the hardware and the quantity of the qubits and getting more capable systems. You had mentioned earlier about the idea of IBM's hybrid cloud. Where quantum kind of exists in and among all these other types of computing. And we kind of identify, you know, this is a problem for quantum. This is a problem for AI. This is a problem for, you know, classical supercomputers, whatever it is. Is there a particular, and I guess it might be hard to choose, but is there a particular problem for quantum that you think about as capability increases in the next few years that you think will be game changing?

Jerry Chow :
We're already seeing it, right? We're already seeing it in terms of like this past year. Last year, we had a, you know, we call this architecture where it all comes together, right? With classical and quantum, this quantum centric supercomputing. vision, right? But you know, this past year, we already had working with our collaborators out in Japan at the the Riken Institute, right? And we were able to look at molecular simulation experiments on, on complex molecules that are beyond exact simulation, right beyond what you would calculate exactly on any kind of classical machine, right, which you would typically use you know, the, the, the most high performance HPC type systems to look at, you know, leveraging approximate solutions. And we were started looking at problems, they're leveraging high performance computing plus quantum computing, right. So we're actually able to look at these these iron sulfide clusters, right, these molecular structures that are, you know, of interest and of a particular complexity, that for one part of the problem, where we actually were able to map the problem onto a quantum circuit and run it on our hardware, leverage 77 qubits of our Heron processor. But then the follow-on to that is that we need to take that data, we need to pass it through a sampling algorithm that runs on the Fugaku supercomputer that sits in the Reckon Institute. using 6400 nodes of their custom ASIC high performance cluster. And that sort of back and forth happened many, many times until we get towards a an energy diagram. And in this case for that for that problem, that's comparable in terms of the solution quality to what you can do using high-performance computing alone. So this is really this realm that we're in. We call this the utility era where quantum and classical are going to be working together to push and compete against sort of the best approximate methods that you might have using classical.

Ian Krietzberg:
this kind of combination that you're talking about of switching between quantum systems and normal systems, it kind of answers a question that I had that's been on my mind a lot. Because we talk about the promise of these technologies, right? In a lot of cases, these advanced algorithms are helping out corporations doing specific things in that world. But increasingly we're hearing about the promise of what they're capable of. We hear about precision health care. We hear about finding new molecules. And you see systems and algorithms that kind of bleed into the AI world, like AlphaFold, that are focused on that side of things. And it seems to me that in some of these cases, the promises of quantum and the promises of AI, which seem to be kind of rising at the same time, kind of overlap when you're talking about healthcare and the molecular side of things. And I guess it's just interesting the way that, you know, you might predict them continuing to work together. Is there a world where AI gets processed, AI algorithms get processed on quantum hardware?

Jerry Chow :
I think, you know, quantum plus AI is certainly two very hot topics. And, you know, the combination of which are also very, very on the top of mind for many people. Right. And there are, you know, there's almost two fronts to this, right? There's one side is like using AI to help us with quantum. That's, you know, almost something that is already happening in terms of, you know, code assistance and things like that. We've, you know, worked on a Qiskit code assistant that leverages Watson X, for example, as an example. And then, you know, there's also leveraging AI models to help map quantum circuits more effectively onto hardware, right, that can be more efficiently run, especially at a particular size and scale. And then there's the flip side of it, which is how can quantum really influence where we are with AI? That, I will say, is really a ripe area that's a constant source of a lot of theoretical exploration, right? And, you know, there are some threads there of looking at using quantum to help with classification tasks, right, especially depending on the types of data that's underlying it. It's, I mean, dependent on, you know, there can be some very, very structured data sets that are very challenging to classify with any kind of classical support vector machine, right? But if you leverage a quantum, machine learning type of algorithm can actually be very fast, right? But it's, I would say that there's a lot there to still be explored. And the best thing we can do is to, you know, to your point, place them in this kind of compute architecture that allows one to leverage them, right, side by side. Right? Because, you know, I think that's the key thing, right, which is that we see this whole vision of quantum centric supercomputing and this future of computing as allowing you to have these multiple modes of computation, right, that can feed one another. And just like we're starting to do this with the HPC community, right, to find the right types of problems that map onto quantum circuits, we're gonna have to explore what are the right types of problems that map some part of it into tensors and GPUs, and then another part that maps into quantum circuits and bring it all together.

Ian Krietzberg:
You know, since you just mentioned GPUs, an interesting part of AI Related to those GPUs and the hardware that kind of powers it is just the the kind of sheer cost You know not it relates to dollars, but I'm really talking about the cost of energy and I just wonder how quantum how quantum hardware fits into the kind of energy efficiency side of things is it highly energy intensive to power quantum computers and

Jerry Chow :
It's actually not, right? I think that's the one thing, especially with our types of systems that we're building, which are super-connecting, qubit-based. They do need to be cooled down with dilution refrigeration technology. I think on your tour, you probably saw what some of these dilution refrigerators look like. But in terms of a lot of the energy footprint for those types of systems, the cooling side is actually not that much. Today, you know, there's probably a fair bit that goes into some of the classical infrastructure that supports it, right? But over time, we're also looking to bring a lot of that classical infrastructure inside the cooling system itself too, which then, you know, obviously brings down the total energy dissipation of that as well. So, in many ways, for the types of computational tasks that we're looking to do on a quantum computer,

Ian Krietzberg:
It's actually very energy favorable to use quantum computing. The cost side of these things is always multi-layered, right? And that's one side. The other side, when you look at the kind of chip construction of classical computers, putting the hardware together itself is energy intensive or has implications of sustainability because it requires certain minerals that the mining process isn't maybe a wonderful thing for the environment. Because we're dealing with different hardware here, how much does that carry over when we talk about quantum and the kind of environmental cost, I guess, of building a quantum computer?

Jerry Chow :
Very different materials, certainly, right? I mean, we're talking about superconducting materials that are relatively internationally rare. Probably the one The one, especially for the, from these systems that require cooling, the one more rare material is something like helium-3, which is an isotope of helium, right? Not the kind that you put in your balloons for birthday parties, but ones that, you know, isotope of helium, and it's required to cool down to the low temperatures that we have, right? That's certainly one piece of that broader supply chain that, you know, we're certainly mindful of. But there are also very, very sort of efficient ways of leveraging that, especially as you scale out to larger system, you know, data center type of cooling infrastructure does not something that is like a use it and lose it type of thing, right? There are ways of building these systems, which, you know, have closed cycles. And, you know, we're not just exhausting the world's supply of this type of resource. But overall, you know, from the other end of, you know, rare materials, it's not necessarily, you know, it's not, it's not certainly matching the needs for say, building EV batteries, right?

Ian Krietzberg:
I want to talk as well, I guess that's somewhat of a bridge to it about the kind of ethics of these more powerful systems. And I guess your ethical consideration as we're working to build them, and moving to that era of utility and maybe ubiquitous quantum computing, there are some physicists who don't think quantum should be developed. And I'm curious, you know, if I was and I am not a physicist by any means, but if I was a physicist saying, Jerry, I don't think this is a good idea. I because of how it might be leveraged by bad actors, you know, maybe it's too powerful. I wonder what you would say to me as someone who's, you know, building.

Jerry Chow :
I mean, to me, that's a, it's part of just technology development and, and, and improving the tools that we have in, uh, from a computational standpoint, right. Uh, it's, it's not, it's not necessarily anything that we should be treating very differently from how we're looking at being responsible about how we are, you know, leveraging, high-performance computing today and also, you know, AI today, right? We are, if anything, we can be a little bit early here. We've already set up some responsible quantum computing initiatives at IBM, right, to make sure that this is done in the right way, right? Of course, in a way that does, allows us to progress and while not stifling, you know, innovation. But to me, really, like, there's just so much to lean on in terms of the evolution of computation in general, that it's not necessarily anything to be treated on its own very differently, right? That is part of this, as we've mentioned already before, that is part of this broader computational framework, right? So you're not going to do any particular carve out just for this particular technology. I think it's all, it's overall framework for how do we treat equitable access or, you know, fair access to computation in general.

Ian Krietzberg:
The other side of that that I think is starting to have people a little bit concerned is the idea that quantum can break encryptions, the kind of encryptions that, you know, today tie the world together, right? I guess, how real is that threat on the timeline of what should people be doing things? now, today, getting things in place to prepare and safeguard against that?

Jerry Chow :
Yes, absolutely. Time is now, right? I think the point is that there are safeguards. And, and, and, you know, knowing that fact, right, that there are quantum safe encryption protocols, right. And then knowing also, roadmaps for certainly, you know, ourselves, as well as other entities out there that are projecting the scale of this technology. basically says that enterprises, organizations should be looking at quantum safe encryption standards today. And, you know, at IBM we have a quantum safe, you know, software business that helps people figure this out. But to me, you know, it's one of these things where obviously it's an inertia thing, right? That, you know, you don't want to be the first one to do it. You almost need the industries to move together, right? A lot of that is the communication and we see already the rollout of the standards. And so you're starting to hear it at the highest levels in terms of information security. even from our government, right? Those are the protocols to be used and leveraged. And we need whole sectors to move and not just individual pieces.

Ian Krietzberg:
And that's a movement that ought to be happening very soon, I guess, because the eventuality of the encryption breaking is on a very near term horizon.

Jerry Chow :
Right. I mean, like, you know, over the next decade, these types of scale out of these systems will be at scales that we can start looking at problems like Shor's algorithm, right, whether it does it at full scale or not is something obviously, we will see when we build them. But the point is not even to get there, encrypt in, change your data security already, right, so that we don't even have to worry about that.

Ian Krietzberg:
We're talking about this idea of quantum is kind of on the verge. It's been building for a while. It's been gaining traction. And like you said, in the next decade, we're going to see more of these. It's going to be more of a norm. As these things become more of a norm, as they become more available and accessible and capable and usable, I'm wondering how you think. I know we talked earlier about benchmarks and measurements and identifying problems designed or best suited for quantum to solve. I wonder how you think about the limitations of that kind of near-term futuristic scenario. If we can identify what problems quantum is good at, what is it not good at? What might it never be good at?

Jerry Chow :
I think it's never good at replacing a lot of the things that we do with our computers today, right? It's not built to do the tasks that we're doing today, like, you know, calling on this video or sending emails, right? And, you know, a big part of that is to emphasize also from the, you know, multi-computational platform standpoint that you need all these pieces working together, right? You're never gonna give up your traditional semiconductor-based computers. And you're not going to, you're probably never going to give up this, the GPU frameworks, right. For, you know, tensor based types of problems. And, you know, this becomes another tool that is specifically best designed for problems that are best written in the language of quantum circuits. And that's the best way to see it, right. That it's all comes together. For me, I think, you know, a fun thing to say is that, like, we would love as this, as these things become more, obviously more mature, and we continue to scale out, that over time, the abstraction should drive us so that users don't even know that things are running on a quantum computer, right? I mean, today, when you're running something on your when you ask Siri something, or you ask, you know, any of these AI agents, how much of it is being run natively on your phone and how much of is being run on some GPU cluster in the cloud, you don't you're not you don't know, but it all gets stitched together in the result that you get. And we want to see that too, right for, obviously, a much broader and more diverse set of problems that we now will be able to solve. But now parts of it would have been done on a quantum computer, and you might not even know it.

Ian Krietzberg:
Well, that would be something. And yeah, like you said, right, we have no idea what's going here, what's going to a GPU cluster in the cloud. Everything just kind of gets bundled in and stitched together. That'll be worth watching. And I guess kind of related to this idea of limitations and targeted usability. You know, quantum isn't some sledgehammer that we're gonna hit every single problem with, like you said. It's targeted use cases. There's a lot of hype around about, I don't know, it's about all technology really. You see the hype if you go on Twitter and just scroll on the algorithms for a little while. There's a lot of hype, some of it's super positive, some of it's excessively negative. I wonder how the hype helps or hurts efforts to build quantum. Is it a good thing because it raises awareness? Does it hurt because it might misinform people's impressions of capabilities?

Jerry Chow :
How do you think about it? It's certainly a mixed bag, right? Obviously, you like people talking about the technology, right? And there's always the concern of overhype and sort of prognosticating failure by trying to overpromise. But a big part of this for us is just stick to our roadmap, show the capabilities, and bring in the adoption with users. you know, at a fundamental scientific level, and then certainly at an enterprise level, right, just as we scale out to leverage the tools. And so, you know, for us, we take a very practical stance on this, which is that, you know, there's there could be all that hype, but in the end, it comes down to like, what are we really actually bringing to the table as a computational platform? That's why demonstrations like we talked about earlier with the RIKEN problem become so tangible because now it speaks to this other community of users that literally in the past, all they did was look at what can I run on a high-performance cluster. Now they actually need to ask the question, well, what if I actually had quantum as part of that, right? now they're looking and they're going to be paying attention to our roadmap and saying, okay, well now they're, they're improving the quality like this. And then, you know, in two years, they're going to get to this. And these are real points that we want people to use. Right. And so like my best, you know, my people will have to say whether it's, you know, 20, 30 years away, You know, some people want to say that, right? But our roadmap says where we're going to be. And we expect to have the adoption level and the value that we can bring towards challenging computational problems, right, in a defined time. And the proof is in the pudding in terms of what we can build and what we can really deliver.

Ian Krietzberg:
Now, since you mentioned that, right, you have a specific defined timeline. You expect to derive the value from, I guess, this investment. I wonder what you can tell me about the scale of the investment, you know, for you to achieve the kind of adoption and use of these computers that would make it make the investment in quantum that you've undertaken pay off? What scale of adoption are we talking about?

Jerry Chow :
Uh, you know, we've certainly 2016, right when we first placed the system on the cloud and we were just excited at that time that we were getting, you know, on the first couple of weeks, several, you know, hundreds of thousands of users, uh, over the many years, over the many years, we've gotten to, you know, close to over half a million, you know, users of our platform. Our open source Qiskit platform has, you know, is the most popular one and preferred by many quantum developers. And then, you know, at a scale of the quantum network, which is our, you know, you can think of it as our business ecosystem, you know, we have over 280 members. And so we've driven that adoption to this degree currently. But I think it's still there's still way more to go, right. And, you know, what is the right level, we are, you know, we're obviously doing, you continue to scale in terms of our footprint in terms of like the number of systems we have out there, and especially at this 100 plus qubit level. They're constantly being used to write like, so that's the thing, like, you know, they're always up, and then they're also always constantly being up, but we're going to be very demand driven. And we're prepared to continue to drive it. And so, you know, I guess to put it there, it's like the answer to your question sort of is like, we want, we're very, you know, we're not just putting things out there and hoping that people are using it, right? We're putting it out there with those various strategic intent of, growing adoption and opening the funnel to, for example, this new HPC market and these HPC users to say, get in there. We think that this is going to be a useful tool for you. And then we will obviously increase the number of systems that we can put out there to capture that user base, right? So we're very driven by exactly the user base and the demand that we have, right? But, you know, to me, I think that's this key next piece, which is this quantum-centric supercomputing vision, where it's an untapped opportunity with all the high-performance computing users. And, you know, for HPC in the start, it was very much for scientific compute to begin. And this is exactly what this is going to be useful for in this near-term phase, scientific compute. to discover new algorithms that really inform those next level enterprise types of applications, right? So, broadly speaking, I think, you know, we have this opportunity to keep driving adoption in the near term at that level, right? Engage the hyper-range computing community. and really open the door towards longer term enterprise value.

Ian Krietzberg:
It's a really cool moment to be kind of observing, I guess. And so you guys aren't the only ones investing in quantum. There's a few other people, mainly the big tech folks with the kind of capital to invest in it. And since I got you here, I have to ask you about this one thing. So Google recently unveiled a quantum breakthrough, they called it, with their Willow chip that, according to them, completed a computation of five minutes that would have taken a classical computer 10 septillion years. Now, that's not the part I wanted to ask you about. In that, Google said that that breakthrough, and I'm quoting here, lends credence to the notion that quantum computation occurs in many parallel universes. And I just, you know, we're talking about multiverses that just, I know nothing about this.

Jerry Chow :
You don't need to know anything about it.

Ian Krietzberg:
I'd love to hear what is going on with that. Is there any consideration in all the, I guess, work that you're doing on building quantum about messing around with parallel universes and the multiverse theory? Is that relevant?

Jerry Chow :
Not relevant. That's just buzzwords in order to describe It's like the concept of quantum entanglement, right? Quantum entanglement is this feature that in some interpretations are things happening in parallel universes. But, you know, the whole point is that in the end you measure and you still get tangible results out of your system. And, you know, I, again, like we've talked a lot today from the point of view of what we feel is tangible, practical, usable, computation, right? And I'm not, I'm not at least of the mind that we need to hype to that level, right, of obviously something like multiverse is very buzzworthy from the point of view, especially if you're like a Marvel fan or something like that, but not relevant when it comes to the discussion of how do I use this as a tool? How do I use this to advance what I can compute? How can I start to leverage it for you know, my scientific interests, for my enterprise interests, to really make an impact on my business, especially as this technology continues to advance and continues to, you know, scale in terms of the number of qubits and as well as the quality of the execution.

Ian Krietzberg:
Yeah, it's that hype that we were talking about earlier, right? It gets, it's so easy for this stuff to get entangled with science fiction. And for the lay person, it gets hard. And I think it's getting increasingly more difficult to differentiate what bits are science and what bit is sci-fi.

Jerry Chow :
So, you know, it's why, you know, having these types of conversations to help set the record straight, I think are great.

Ian Krietzberg:
Exactly. And yeah, so I guess just just to wrap up, right, we're setting the record straight. In setting that record straight, for people who maybe have heard the word quantum but don't want to get involved in it, it's too confusing, is there a single big takeaway for the layperson to understand about quantum, understand about what's coming that you think should be top of mind for everybody, regardless of physicist or quantum mechanic or not?

Jerry Chow :
Yeah. No, I think, you know, it is certainly good for the layperson to understand that quantum is going to be a revolutionary computational tool, right? That it will greatly expand the types of things that we can look at, that we can solve, that we can, you know, reasonably rely on computers for. But we need to think about it within that lens, that it augments our capability of computation. And it's not meant to be anything that replaces what we have with our computers today. It's not meant to necessarily be a replacement for AI or anything like that. It'll all come together in terms of just increased computational capability. And in areas that are really of, you know, certainly societal benefit, right, like materials, like drugs, like, you know, uh, understanding our natural world better, right? All these types of things, huge potential, but also I think something which is like something that's already here to try, right? So there's this phase of like the promise that we want to get to, but there's also this phase of probably can already start to find like useful things today. And it's already capable of being paired with, uh, the most advanced computers today. And that's where, If you want to take that step in and not just be a lay person, but dive in, there's tons of resources to already get started. Open source software, software programming, free access to some of our most advanced systems. Right. And there's a lot to learn.

Ian Krietzberg:
It is certainly an exciting time, Jerry. I really appreciate pulling you away from the quantum front, uh, here to, to talk to me about what's going on there. Uh, I, I feel like I learned so much. So thank you so much for your time.

Jerry Chow :
You're very welcome. I had a great time here.