Oliver Dial of IBM: Quantum Advantage Is Happening This Year

0:00Is there are some problems you can potentially solve on them that you could never ever ever solve on a glass of a computer. Remember one end of this wire is up at room temperature where you and I live. And the other end of this wire is at 0.02 degrees above absolute zero. This year, 2026, we're hoping to demonstrate what we call quantum advantage. And I decided this technology was just so cool because... Okay, so Oliver, it's great to see you. I met you, as I said before, once you were fiddling around with a quantum computer back in one of the rooms there. And I wanted to talk to you about where IBM is today with quantum.

0:44There's a lot of chatter, and it's very hard for a non-expert, which I certainly am to understand what's happening. At that time, there was a fairly aggressive timeline. And I was with Jay Gambetta, your colleague, and he was pointing out that you guys had met your milestones so far on that timeline. I think, if I'm not mistaken, this is a year that you're to get to quantum utility. Is that the term you use? We use the term quantum advantage, actually. Quantum advantage. Okay. So can you, I guess, first of all, start by introducing yourself, introduce yourself to listeners and then a little bit of your background, how you got to IBM Quantum, and then we'll go from there.

1:38Yeah, absolutely. So my name is Oliver Dial. I'm a physicist. So I study what's called condensed matter physics. I usually tell people it's physics of rocks, but it's really the physics of sort of how quantum mechanics acts when you get it in really weird circumstances. and I got interested in quantum computing kind of indirectly. I was studying something called quantum dots, which are little boxes you can put only one electron in, and it turns out that's one way people try to build quantum computers. So I sort of transitioned from studying quantum dots as physics objects to studying quantum dots as qubits, and I decided this technology was just so cool because it kind of brings together computation, which I'm really interested in, electrical engineering, physics, all into this kind of one package that has the potential to really change the world.

2:27And so once I kind of decided that was an interesting thing, IBM was definitely the place I wanted to go to to do it because IBM has made a really big bet on quantum computing, that it is, in our mind, part of the future of computing. And I decided that if I wanted to get more involved in that, that it was just really the place to go. At my heart, I'm a hardware guy. I'm happiest when I'm in the lab turning a wrench like you found me or programming trying to get one of these machines to do something unusual. But these days I'm acting as a VP of quantum systems, so kind of responsible for leading the team that takes all the pieces of these quantum computers, puts them together and gets it to work as one big piece thing.

3:09And that's a complicated job because these quantum computers have little tiny quantum core. And then as you work out from that, there is all this classical infrastructure. There's refrigeration, there's control electronics, there's wiring, there's connectors, and all those things have to work just perfectly for these processors to work as well as we want them to. Yeah. And the IBM is a systems company at this point, and for quantum as well as classical hardware. And you have a roadmap. up. Where are you on that roadmap right now? So yeah, the roadmap you're talking about is something that we publish and update every year, and it shows what we think the future is going to look like reasonably, and what we did in the past.

3:59And kind of one of our big statements is we've generally stuck to it. And the reason why that's important is there's a lot of hype in this field. There's a lot of people saying they can do crazy things. There's a lot of people making just very optimistic projections and so what we want to be able to say is look we've been doing this for years we've been making these projections for years and you know we we changed a little things but we're basically on the track that we've been saying that we're going to be on and so you can trust us in these predictions about the future a couple of years ago we hit a point on that road map that we call quantum utility which is what we started this conversation on and it's a really neat point it's the place where the quantum computer can do something complex enough that it is challenging for a classical computer to simulate the quantum computer, right?

4:43And so if you, you know, the background to why that's important is quantum computers don't work very well. They're expensive, they have high error rates, they're not very big, they're hard to program. And the reason why they're interesting is there are some problems you can potentially solve on them that you could never ever, ever solve on a classical computer. And so it's worth all of this rigmarole to get that point of I can solve something I couldn't do otherwise. That's really the lure. And so being able to get to the point where you can't simulate the quantum computer on a classical computer is sort of table stakes.

5:18Until you hit that point, anything that you did on your quantum computer, you really should have just done on a classical computer instead. And hitting that point requires kind of two things. One is you have to have enough qubits. That's the unit of computation for a quantum computer. And it's important because the amount of memory it takes to directly simulate a quantum state is exponential in the number of qubits it has. And so if you don't have enough qubits that it couldn't fit in the memory of a classical computer, that opens a set of pretty efficient simulation algorithms. The other important thing is how many operations that you can run on the quantum computer.

5:53I said it has a high error rate. And so if you run too many, the machine will make too many mistakes for us to correct. And the reason why that's important is there are also simulation techniques that you can use for circuits that are short, that are efficient. So 2023, we've hit the barrier of the point where the quantum computer becomes challenging or impossible to simulate on a classical computer. This year, 2026, we're hoping to demonstrate, or we're hoping for someone to demonstrate, ideally not us, what we call quantum advantage. which is the point where you're using a quantum computer to do something better, faster, cheaper than would have been possible on a classical computer.

6:32And it's a little bit different from being able to run something that you couldn't simulate, because now you actually have to get an answer out of it. It's not just enough to, you know, show some speckle pattern or something like that. And really, quantum advantage can be phrased two ways. One of them is a very strict scientific question of, can I prove that the quantum computer gave the right answer. Can I prove that I got it faster than I possibly could have on a classical computer? Can I make a rigorous proof of this claim? And that's considered sort of an important scientific issue because that settles the question of whether there's some computational regime that we can practically achieve that was unachievable on a classical computer.

7:12There's also a very nitty-gritty, call it heuristic advantage. I ran this on a quantum computer and I got a better answer than I could have gotten on a classical computer and I'm happy with that. The reason why we think these things are going to happen this year, well first of all people have already claimed that nitty-gritty heuristic advantage over the last year or so, but the main thing is that we think that the computers have advanced the point, the algorithms advanced the point, and the tricks for correcting the noise have advanced the point where this is going to be possible for a wide variety of users on our systems today.

7:45And so you know we're really hoping for this collaboration where we bring the hardware really smart scientists bring the problems and then the flip side of the coin really smart scientists also try to solve these problems on classical computers because of course what we're going to find is when somebody does something called quantum computer a whole bunch of computer scientists are going to go to work with their ai coding agents or whatever and try to replicate the event on a classical computer and so we need this conflict we need this back and forth between the quantum computers and the classical computers to come to a bit of a close before we can really claim that.

8:18Yeah, and quantum advantage is one of the confusing things because Google claimed the quantum advantage, I think, two or three years ago. And, you know, different companies, I think, mean different things by quantum advantage. So from IBM's point of view, and also you talked about the number of qubits. explain why the number of qubits is important and how many qubits are you working with at this point and how do you create a qubit in your system? Because there are different kinds. Great questions. I'm sure you've heard the phrase, there are lies, darn lies, and then there are benchmarks. Yeah.

9:06It's a little bit like that. One of the big questions on quantum advantage is verifiability. can you show that the quantum computer actually produced the correct output? Because it doesn't really do you any good to say you solved a problem if you can't say for sure that that happened. And so a lot of the back and forth around Google's claims on that are around verifiability, that they solved a random circuit sampling problem where there's supposed to be some distribution of outputs that's hard to compute classically. And they're able to show that sort of for smaller circuits, the distribution had some measurable fidelity.

9:44Sometimes it gave the correct answer. The number was kind of one time in a million or something. And then they ran a little bit bigger problems and said by extrapolation, we should still be getting the correct answer some fraction of the time. And honestly, it's probably true. But if you want, again, if you approach this from a scientific question, it gets a little bit dicey making that claim because you do have that extrapolation step in there. There are some replicas of that that have kind of bigger differences between the quantum and the classical runtime at smaller circuit sizes where you really can simulate it.

10:16So maybe that claim has gotten stronger. But again, it's fuzzy because of that verifiability aspect. What we've tried to do is help set up something called the quantum advantage tracker, which is like hugging face for quantum in some sense. It's a place where people can post specific problems, performance on a quantum computer and performance on a classical computer and really let this debate happen out of the open and it's actually started to get quite a few events put on there. Is this a leaderboard that you guys publish? It's a leaderboard we initially set it up but you just you make a issue on GitHub to add something to it it's not like we're not like we're editing what goes into it that it comes from the community.

11:08Yeah. And then on the number of qubits and the kind of qubits and... Oh yeah, so many questions there. So quantum computers are a little bit different from classical computers or at least they are today. That you have memory and you have a CPU and you have storage and these are all very distinct things. Think of a quantum computer as an accelerator. It sits off to the side of the classical computer, it's controlled by the classical computer and so we send in a classical description of a problem. We manipulate the quantum state inside of the quantum computer and then we measure the qubits, which is a way of converting it back into classical data that we can pull out to our classical computer again.

11:43That's really important that we have to begin and end with classical data because that's the only thing we have access to as human beings. So the number of qubits describes how complicated of a quantum state you can manipulate in the center there. And basically you can think of it as a computer for doing linear algebra if that makes any sense to you matrix multiplications in an enormously large state space as long as you're kind of manipulating inside of this quantum date um this um this quantum device and specifically it's a vector space that has two to the number of qubits dimensions in it um so complex vector space so that's why the number of qubits is really important right if that vector space is too small you can't represent the problems you're interested in And you can, like I said, just do it on a classical computer anyway.

12:33If it's small, like four qubits, I could do on my phone quite trivially. So to get this to work, we need to have a way of storing information that the universe can't see. Because if you know anything about quantum mechanics, you probably heard something about collapse. That if you have something in a superposition, and in the case of our qubits, that might be a superposition of zero and one. If you look at it, it gets to decide I'm either zero or I'm one and I'm back to classical information again. And so I have to have a way of doing something that looks a little bit like digital computing because I have the zero one state, but that's very, very well isolated.

13:06And there are a few dozen ways people do it. One way I mentioned at the beginning with my background that you can take a single electron and you can store one and zero is two directions of what's called the electron spin. Basically, electrons have a little magnetic moment, just like a bar magnet. They can point up or down. And because that's a really weak magnetic field, it's hard to see which direction the electrons pointing in and if you're careful enough about it you can actually make it impossible to see for a little while and so you can use that to store quantum information at IBM we do something a little bit different we use what are called superconducting qubits and in superconducting qubits what we do is use metal superconductors to make little resonators LC oscillators think of it like a pendulum but the electromagnetic version and we can use those to store zero microwave photons or one microwave photons and those are our two states.

13:58And so keeping that hidden from the universe is a matter of making sure the photon can't leak out of the box that we put it in, this little resonator, and making sure that it doesn't get absorbed by anything. The reason why we do that is superconducting qubits, it turns out, you can make using the same tooling and processing and approaches that you used to make classical digital logic. We use the same nanofabrication tools, we use the same clean rooms, we use all the same techniques for packaging, it's just at the end we make a device that we cool down to near absolute zero as opposed to a device that you put into your laptop over there.

14:33So it's a really good match for the types of skills that we have here at IBM and they're also sort of the leading modality as far as the number of qubits that we can build and leading or near leading as far as what are called the fidelity zero rates that we can get. You asked about device sizes. Currently our kind of flagship devices that we have in our fleet are 156 cubits. To put that in perspective, the Google supremacy experiment you mentioned was done with 53 cubits. Around 100 cubits is where it gets basically impossible to do classical simulation someplace between 50 or 100, so we're kind of significantly past that threshold.

15:14We have done experimental devices that are quite a bit larger. A couple of years ago we made a device called Condor that was a thousand cubits just to prove that we could pretty much. It's not still online. We actually took it apart almost immediately because the error rates in it were not low enough to productively use all thousand qubits. And so it was sort of an expensive technology demo, not something that we thought would be useful to our clients. We do have a new device that's coming online this year that's called Nighthawk. We name all of our devices after birds. And that's actually 120 qubits.

15:47It's a little bit smaller than Heron, but it has much higher what we call connectivity. If you want to run calculations on these quantum computers, you need to get the qubits to interact with each other. Think of it as if you're doing classical logic, you can do anything that you want if you have say, and not, and exclusive, or actually, or is enough. Sorry, I don't usually do classical logic. It's the same thing for quantum logic. There's a gate set that you can use to construct any arbitrary computation and one thing that you need is a way to entangle two qubits basically. We do that with literally wires on a chip with what we call tunable couplers so we can turn on and off the interactions between the qubits.

16:28All of our previous devices had at most three connections from each qubit and so if you wanted to do it gates between qubits that were far apart on the chip you have to swap swap swap move the data around. Nighthawk has four wires instead of three which doesn't sound like a lot but it's a big improvement and so you can do computations more efficiently on it even though it's got a slightly smaller number of qubits yeah and i don't want to turn this into a course on quantum computer but but uh your qubits are are photons so um so i'm a physicist i call a lot of things photons okay um so our qubits are superconducting resonators so an lc circuit if that means anything to you um that basically a little patch on the chip that has a capacitor, two pieces of metal that are close together, and something called a Jost's injunction in between that acts like an inductor.

17:21And so the energy alternates between electric field between those capacitors and phase, basically current inside of the Jost's injunction, back forth, back forth at a frequency that's set by the design of it. We designed that frequency to be about five gigahertz. So it's a microwave frequency. And just like optical light, if you go cold enough and look carefully enough microwave light has photons and so we can store either zero or one photon inside of that little teeny tiny electromagnetic resonator yeah so the the data is a photon or no photon but it's stored in this little superconducting box and and that superconducting box is one qubit one cube and so you have to build an array of now 156 qubits why is it So it sounds like an engineering problem, but why is it so difficult to expand the number of qubits?

18:17Is it the noise problem, the error correction problem? There are a lot of problems. That's what makes it so much fun. So one thing to realize is although we're using semiconductor technology, these qubits are actually pretty big. Ours are about three quarters of a millimeter on a side. So if I handed you one of these chips, you could actually see our qubits with your naked eye. I mean, to me, that's amazing. We're talking about entanglement and superposition of things so big you could see them, although I'd have to freeze you to death before you got to see it happen. So some of the things that makes it really challenging is that we have a lot of IO.

18:52If you think about your microprocessor or a bit of memory, it has a few hundred pins on it, but then it has billions of transistors and millions of bytes of memory. For our devices, for each qubit that we have, we actually need a wire to send microwave signals into it to control it. And then we need another wire for each coupler that it's connected to, to control it. And then we need another wire for each group of qubits to measure them. And so our 156 qubit device, I'm going to get the number wrong if I try to do off the top of my head, it's easier for Nighthawk. Our 120 qubit device has on the order of 480 control signals going into and out of it.

19:31And those are not digital signals, those are analog microwave signals. And so figuring out how to pack that many signals into a small area in a way that's low crosstalk and accurate is extremely challenging. But then we also need 480 signal generators to make those. And each one of those, it looks a little bit like the transmitter in your cell phone, but it needs to be much more precise, much lower noise, so it's a little bit of a specialty object that's what we call the control electronics and then we need the wiring to connect these things together which sounds like okay wiring you know big deal oliver except remember one end of this wire is up at room temperature where you and i live and the other end of this wire is at 0.02 degrees above absolute zero so a little bit colder than deep space and so the thermal gradient across this wire it's a little bit like trying to air condition your house with all the windows open if you make that wire wrong it carries a lot of heat into our system and so we need to be very careful about how we dissipate that heat down the length of the wire.

20:31So those are just a few of the engineering problems that you need to solve as you scale. All of these you know they add complexity to the system but as you said we're sort of a systems engineering company at our heart that this is the kind of problem that we're used to dealing with. So I understand tunneling diodes and when you bring it down close to absolute zero what you're saying is the the photon in that junction stops moving? So the junction works as long as you're colder than TC that as long as the superconductors are superconducting. For the materials we use, colder than about one degree is enough.

21:20The reason why we need to be so much closer to absolute zero is actually thermal photons. If I had an IR camera and I looked at right you right now, I would see you were glowing. And if something is glowing at the frequency of our qubits, it's adding photons to it and so again it messes up our quantum computer. So we need the chip to be so cold that it's not glowing at five gigahertz and that's where that is absolutely way colder than deep space numbers come from is not so much from getting the Josephson Junction to work or even the qubit to work but just protecting it from infrared radiation if I said infrared radiation I should said heat radiation at microwave frequencies right as for the photon so we there's a lot of different types of superconducting qubits as well I should mention the store the information in slightly different ways the description i gave you is appropriate for a type of qubit that's called a transmon and that looks like a capacitor shunted by a jose injunction and in the those trans bonds what you're what you're doing is basically keeping the jose injunction in a place oh gosh i'm trying to figure out how to explain this in a simple way um jose injunctions have what people call a phase current relationship that there's this relationship between how much phase is dropped across it think of it as a little bit like magnetic field and how much current is flowing through it and that change phases whenever there's a voltage across it and so if you think about it what I'm saying is that voltages drive currents inside of what Jo's injunctions but it's really voltages drive derivatives occurrence if I put a voltage across it the current slowly goes up I reverse the voltage the current slowly goes down so in electrical engineering terms it looks like an inductor in this limit you know as you know I'm sure you can make these things do all kinds of weird tricks this is just a particular way we use them the cool thing about it is it's a ridiculously nonlinear inductor and the reason why that's important is you I talked about zero photon and one photon you never said well what about two or three or four or five or six and the answer is qubit shouldn't have a two state or a three state or a four state or a five state or a six state, that we really need a way to isolate that zero photon and one photon manifold.

23:37And the way that we do that is the Jost injunction because it is so ridiculously nonlinear, we can make our oscillator so that if we're putting one photon into it, that takes five gigahertz of photons, but the second photon has to come in at 4.8. And so we can make those frequencies so different that you can really isolate those bottom two states. I'm not sure I really answered your question very well there. Yeah, but that gets me there. Yeah, I've never tried to explain this without a chalkboard and a lot of diagrams.

24:10The issue to get... So you have those 156 qubits, and you say that they're particularly useful for matrix multiplication, is that...? Well, it's the operations that you go on them all have representatives in linear algebra that most of our gates you can write as unitary transformations which are basically matrix multiplications that don't destroy any information that they have a inverse a little bit more complicated than that but good enough for our purposes it's just again they're in this sort of ridiculously complex or ridiculously large space that you're performing them. Yeah. And in that I'm interested in my podcast is primarily about AI.

24:59How does that matrix multiplication relate to AI? I mean, is there an application? Will you be able to run artificial intelligence algorithms that depend on matrix multiplication on a quantum computer? So a lot of the time we talk about kind of near horizon applications versus kind of long horizon applications. That there are things that you can do or almost do in our quantum computers today. And then there are things where you would need a very much larger error corrected quantum computer. We really should talk about error correction at some point to execute. So for things that you can do today, we need problems that map really efficiently onto the quantum computer.

25:49And so you're looking mostly at things in chemistry and material science, basically using the quantum computer to solve problems in quantum mechanics. And in fact, there are some chemistry demos now where we're really approaching the accuracy of what you can do with the kind of most up-to-date classical chemistry techniques. So we're really getting close there. In this near-term period, a lot of the time we're dealing with heuristic algorithms because at the end of the day, we're sort of typically competing against classical heuristic algorithms anyway. It sort of makes sense that you're pushed into that limit.

26:22And so the horrible thing is the only way to really test some of these things is to try them and see what comes out. And AI is very much in the category of like the king of heuristic algorithms. And so it's just really hard to say what it looks like to do it on a quantum computer when we don't have one large enough yet to actually represent any of these models or to do a circuit deep enough to run them. there have been some proof of concept demonstrations of something called kernel estimation trying to find a good basis to represent information and so that it's sparse which is a part of a lot of AI models that if we deliberately design a problem so that there is a feature that we know quantum computers can recognize more efficiently than classical computers and one of those is period estimation like if I have some signal figuring out what the periodicity of it is, we can show that the quantum computer can win.

27:17Again, totally just toy small problems, not anything anyone would be interested in. But the problem is nobody really knows how often do problems like that actually come up in real life because we wouldn't be able to tell. So right now, sort of AI on quantum computers is very much in the field of small toy problems and wondering what it would really look like. there are a lot of heuristics that people are playing with an optimization which is sort of one step removed from AI you can kind of think of it as almost a training phase that look really encouraging but again they're heuristic and again not surprising all the best classical optimizers are heuristic so you know we're ending up in the same space in quantum um and I'm just going to stop for for listeners to find heuristic oh heuristic yeah there are a lot of yeah sorry I'm a scientist, I sometimes talk that way.

28:08There are some algorithms where I could sit down with a chalkboard and I could prove it to you. The matrix multiplication, I can sit down with a chalkboard, I can write down the algorithm, I can say this does it. Fast Fourier Transform is a great example. It's a way of going from time series data to what frequencies are in it. I can prove to you that that algorithm works just by doing math. There are other algorithms, Quicksort, another great example. There are other algorithms like a beam search genetic algorithm, radiant descent on neural networks, where I can't prove to you that it's going to give you the right answer.

28:45I could even maybe prove to you that it's going to give you the wrong answer, but in practice they work very well. And so we refer to those as heuristic algorithms, meaning you can kind of look at them and say it makes sense that this should work pretty well. Maybe you can make a bit of an argument why it could work pretty well but I can't prove to you that it's gonna give you the answer you want you just sort of have to try it and see everyone's solutions to traveling sales but problem solving fall into this category as well so an example of you mentioned there are very few quantum algorithms an example of one of those blackboard algorithms is called Shor's algorithm yep and it's a pretty famous one because it's an algorithm for factoring numbers.

29:28That's a problem that's believed to be hard for classical computers. And actually, you mentioned, oh my gosh, I forgot his name, Scott Aronson earlier. He has this sort of famous description of Shor's algorithm of three things are possible. One is that it's easy to factor numbers classically, and somebody's going to get a Fields medal. Another is that it's impossible to build a quantum computer. We're going to figure out why on the way, and somebody's going to get a Nobel Prize. Or the third is that really quantum computers can do things that classical computers can't. And that's kind of an amazing outcome.