The Exchanges

Q one kind or another. Route optimization. Let's say some, like, really complicated route optimization for For autonomous vehicles. I don't know. I'm making something up, but like, the Blinko example seems close enough to me to something that I'm surprised there's a multi-year gap between the time that you can do that Blinko thing and when it becomes a practical set of applications. Like, what's the, what am I missing?

A I really, I, the bottom line in all of this is, is, um, We have in the classical IT world, we have decades, if not centuries of, of classical mathematics and physics, understanding of how the real world operates. Uh, you know, if you, if you look at my favorite example, something called the Navier Stokes equations, if you're designing an aircraft or something like that, you know, you use Navier Stokes equations to figure out exactly how airflow is going to go across a wing or a whole body, um, or, or some particular thing bouncing off the, say the nose of the plane or something. Navier-Stokes equations dates back almost 200 years. It was, it, it only became relevant when machines came along to actually deal with them. Quantum's only been around for 30 years. There was not a great corpus of applications that the quantum hardware base can say, okay, here's what we're, here's what we could do with this. And not only is there not a large, a long history to draw on mathematically, um, quantum Computing algorithms are not generally intuitively obvious. They're not something that we grasp as classical human beings, if you will. We're too large to be considered quantum-based. Um, so it's, the algorithm development phase of this is really quite complicated and slow, and unfortunately, to my mind, underfunded from both government and academic environments. Uh, Peter Schor, uh, who's basi…

AI assessment note: “the algorithm development phase of this is really quite complicated and slow”

Q effort historically has been on the hardware, and you had to solve the hardware problem first before the algorithm is even necessary, and so we're just, like, entering that new phase? Or I guess another way to ask the question is, like, is the hardware good enough? I'm sure it's not perfectly optimized, and it'll get better, but is the hardware now good enough That the attention needs to shift?

A It's, the hardware is now entering that stage, and I don't want to throw too many acronyms around, but the, the stage that we're in right now that is starting to come to an end is called noisy intermediate scale quantum, which means that the word N at the front, the noisy, means that quantum systems are still very, very error-prone. Uh, you don't run a quantum algorithm once and get an answer. You run a quantum algorithm a thousand times, and you get a histogram. Of all the potential, all the output you got, and you hope that somewhere in that histogram, there's one that stands high above the rest. So it's still a statistical activity. We call them shots. You do a thousand shots, and you hope that the answer that you, that is correct, appears 78 or 80% of the time. Ok, that's where we're at right now, mainly because of this concept of error correction. Every time you do something on a quantum system, there is a potential to make a mistake. To introduce some kind of error. To get something that's wrong. And so this issue of error correction in quantum systems is, is probably the most pernicious aspect facing the industry today. How do I build a system that has built in error correction? So the mistakes that are made are somehow basically diminished or compensated for. And so what is happening at this point is you have this issue of physical qubits. People say, oh my God, we have…

AI assessment note: “the hardware is now entering that stage”

Q All right. I want you to start by giving me a brief history of quantum computing. And I think more importantly than the history is where, where are we today? Like, how would you characterize the current state of that market and technology?

A Well, the, the brief history of, of quantum starts with a physicist Nobel Prize winner named Richard Feynman, who basically said it's really hard to, to simulate quantum phenomena on a classical computer. A classical computer is the systems that we're, we've all used from the six hundred million dollar HPCs to what works on our smartphones. And those, the calculations required to simulate or even study quantum phenomena on those kinds of systems can be what we in the sector called intractable. Like the age of the universe to solve a problem on a classical system where quantum offers some significant speed up performance potential. It basically, a quantum system is a sandbox where you can play as if you're operating in the quantum realm, and so that's really where it started in the eighties, and it's progressed over the last 40 years or so, uh, to the point where, uh, there's a number of, uh, quantum hardware systems that are available, uh, at least for kicking the tires. Not exactly commercial yet, and some of the software has actually become pretty interesting, and the key to quantum is incredible performance gains on a narrow class of applications, but some of those applications are really critical to a lot of industrial sectors, commercial sectors, and governments, and, and, and just about everybody, so it's, uh, it's, it's more of an accelerator. It brings computational cap…

AI assessment note: “a number of, uh, quantum hardware systems that are available, uh, at least for kicking the tires.”

Q And I want to get back to the applications, because I'm interested in that too, but before we get there, where are we in the journey of, like, it has been proven, or rather, uh, it has been achieved versus it has not been achieved?

A You know, it's, it's interesting because when, when quantum was first introduced, uh, into the, the world, if you will, and I would say, say maybe this was five or six years ago where people started to think about Hey, what can quantum bring to my advanced computing workload? The, the big question was when. And, and the sector did a very good job of saying, we're not there yet. It's going to take us a little time. And in fact, it may take a decade to get to the place where this becomes widespread, or I, I won't say mundane, but at least something that everybody could, could, could really, uh, take advantage of. We're at that stage where things are, are moving from, uh, science experiments, if you will, from kind of one-off machines that are being Uh, designed and tested in a, in almost a laboratory environment to productization, to the idea that in the next few years, um, in fact, some companies have already started. You can order a quantum system to be delivered to your facility and start to really test where it is. Now, from a performance perspective, they haven't demonstrated what we would call, uh, you know, uh, dramatic performance gains over classical counterparts. It's still in the phase where some people would call them toy problems. You, you reduce the problem to its fundamental essence. You see how well it works, and you say, as quantum computing capability, uh, follo…

AI assessment note: “We're at that stage where things are, are moving from, uh, science experiments”

Q do some bit of work in the first place, and then accept the ratio, or you could reduce the ratio, and you could figure out how to make it 10,000 physical qubits to 9000 logical qubits over time. Are we going to Is the intent to do both of those things, one of those things, or do we not need to, and we don't have to worry about this ratio?

A The thing, the thing is about qubit counts, physical qubit counts, and, and logical qubit real reality, the good thing is it's, it's proceeding, progress is proceeding on all fronts. The guys that make hardware are producing more reliable qubits. There's different modalities that have different, uh, error rates, uh, and, and, and so from From the, in essence, the manufacturing end of this, there's progress being made. So, so what they're doing at the same time is they're making, uh, physical qubits that are less error prone, and they're increasing the number of qubits that they can produce to put into a single QPU, a quantum processing unit. Uh, so some organizations are saying, we have infrastructure. We can scale to a million qubits with the technology we have. It's just a matter of basically engineering it to turn it into a product. Now at the same time, we see architectural, ah, improvements, the way quantum systems are designed, and that is addressing the error, ah, requirements as well. People are becoming more innovative in terms of how you do the error correction schemes. There's, there's different ways to, to look at a whole bunch of physical qubits that are making errors like crazy and solve it, solve those, those, those errors into a logical. So there's, there's algorithmic things going on there. And then the software is becoming more sophisticated. Algorithms are, a…

AI assessment note: “progress is proceeding on all fronts. The guys that make hardware are producing more reliable”

Q Alright, so let's talk about applications then. You, you said, you know, maybe we're three to four years out from starting to see these really hit. Um, give me some examples. Like, what are the, what are the early applications of quantum, commercial applications, valuable, practical applications that you expect to see play out?

A Well, I, I like to think of the, the applications breaking down to three different kinds of, uh, classes, if you will. The first one goes back to the Feynman thing, and it's, it's, it's basically simulating quantum phenomena at some, at some level so you can better understand it. And if you look at how the classical world does certain things, they have to make so many simplifying assumptions to do these, these quantum simulations that you just can't even really trust the results at some deep level of understanding. So, Quantum does things, for example, like I want to design an advanced material that will be so much better when it's used as the, the fundamental material in an advanced battery. Or if I'm looking at a new drug design, I want to understand how proteins may interact. So do I have a better way of designing the particular molecular structure of a protein or some other molecule that serves a purpose in, in drug discovery? Um, things like computational chemistry. How can I better understand that the actual physical interactions at the atomic level? An example in the oil and gas sector is looking at catalyst design. Can I come up with an interesting catalyst that will allow me to make perhaps more environmentally friendly kinds of oil and gas products? Because I understand exactly how those interactions between catalyst and, and materials interact, and I could come up wi…

AI assessment note: “design an advanced material that will be so much better when it's used”

Q out a little bit, and I think it's, it's turned back again in the favor of quantum computing of late. And I wonder, one, do you see the same thing? Do you, where do you think we are in the hype cycle? And two, uh, are we in the right place in the hype cycle? Is, is the world calibrated correctly as to how exciting this time is for QC?

A I, I'm worried about the amount of investment that's flowing into the sector, and I, I, I consulted with one company who was, they were dirt poor, uh, to be quite honest, and I love them because they were cheap, and, and they were, they knew it, and, uh, somebody wrote him a check for multiple hundreds of millions of dollars, and I talked to the CEO and said, what's the plans for this money? And they said, no idea. Um, you know, that's not a good sign. My concern here is the enthusiasm, and, and let me just, let me just give you my, my, my stock and trade answer here. Right now, there are About 85 different organizations, companies aspiring to be quantum computing hardware suppliers. 85 of them. Some of them are garnering some very big VC investments. Um, the concern here is, if you take, if you go, say, the HPC world, or even the PC world, um, or, or laptops or smartphones, there have never been 85 suppliers in the history of HPC. Total. There have never been 85 suppliers of laptops, 85 suppliers of smartphones. There are too many organizations out there, and I can confidently say 70 of them could go belly up in the next two years, and it ultimately wouldn't affect the overall vitality of the sector, because the smart ones will rise to the top, and the ones that perhaps slipped along the way will go away. Now, what happens when 10 companies go under? Or five companies have dow…

AI assessment note: “I'm worried about the amount of investment that's flowing into the sector”

Q Can you just give me more detail? Like, what does it mean to create like an artificial environment that's not relevant?

A Well, it's the idea, and, and, okay, here's, here's the simple example. There's, there's a simulation that was done a while ago. It's called boson sampling. Now, imagine you are in Japan, and you go into a Plinko parlor, and you know, you have the thing where you, you drop a ball down, and it bounces all around, uh, those little pins that eventually falls into a slot. Say I drop a thousand of those, and I say, okay, sim, and, and when you're done, at the bottom of this thing, you have the distribution of where those little Plinko balls ended up. Simulating that on a, on a classical computer, the actual simulation of how every ball interacts with every pin and every deflection and everything is classically intractable. You could do it on a quantum system by basically saying, okay, I've got photons, and every time they hit something, they're either going to break left or right with some degree of predictability. Ok, that's, that is an example of a classically intractable problem simulating a Plinko machine versus one that quantum could do because it, it, it draws on the essence of what quantum is. It's about potential. It's about probabilities and such. Ok, there's no practical application for those kinds of things. So what you end up getting Are, in some cases, in the early days, um, benchmarks that sounded interesting but were terribly misleading. Uh, there was one organization…

AI assessment note: “Ok, that's, that is an example of a classically intractable problem simulating a Plinko machine”

Q that's going to end up being commercial, is supposed to be a somewhat easier task. So there is this kind of fundamental, like, breaking point in the progress that, that becomes, like, a step function. Is that how this should look? Like, do we get to fault tolerance, and that unlocks the world, or is it just, like, steady incremental drumbeat of, uh, More and more fault tolerance over time.

A I, you know, this is, you know, this is the story of, of advanced computing in general, this idea of Moore's law, that things always get better, that there's always exponential improvement. And, you know, I, you could point to a number of different quantum computing vendors who are using silicon-based technologies, trying to really borrow from all the experience of what's going on in the classical semiconductor world to tap into those advances as well. So if you look at What's happening in the sector, it is not a linear progression. In many cases, it is a near exponential kind of capability. So this, this idea of moving from 10 to a hundred to a thousand cubits is, it's, it's going to happen faster, uh, than if you were just on a linear scale. And so that's, that's really why there's so much enthusiasm about quantum. It's because it, not that it offers incredible capability today, but its trajectory Is so much more impressive, especially when you compare it to what's happening in classical. People don't talk, we always talk about the end of Moore's law. We talk about a number of things where it has become prohibitively expensive and complicated and just confusing to have additional compute capability in the classical world. Some of the most expensive HPCs right now doing science and engineering work are costing six to seven hundred million dollars a pop. And they require perhap…

AI assessment note: “it is not a linear progression. In many cases, it is a near exponential”