Oliver Dial of IBM: Quantum Advantage Is Happening This Year

Introduction to Quantum Computing

0:00is there are some problems you can potentially solve on them that you could never ever ever solve on a classic 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

0:33around with a quantum computer back in in one of the rooms there and I wanted to talk to you about where IBM is today with quantum there'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

1:06milestones 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 use the term quantum advantage actually quantum advantage

Guest Introduction

1:20okay 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 and then we'll go from there yeah 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 in a really weird circumstances and I got interested

1:56in 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 object 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 and so once I kind of decided that was an interesting thing

Joining IBM Quantum

2:30IBM 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 you know 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

3:03that takes all the pieces of these quantum computers puts them together and gets it to work as one big piece thing and that's a complicated job because these quantum computers have little tiny quantum core and then as you work out from that there's 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 and yeah the IBM is a systems company at this

IBM Quantum Roadmap

3:34point uh and and for quantum as as well as uh classical hardware uh and you have a a road map uh where are you on that road map right now so yeah the road map 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 and kind of one our big one of our big statements is we've generally stuck to it uh and the reason why that's important is there's a lot of hype in this field there's a lot

4:06of 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 roadmap that we call quantum utility

Quantum Utility and Advantage

4:30which 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 and 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

5:04it's worth all of this rigmarole to get that point of i can solve something i couldn't do otherwise that 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 sort of table stakes until you hit that point anything that you did on your quantum computer you really should have just done on a classical computer instead

5:26and 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 i 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

6:02there are also simulation techniques that you can use for circuits that are short that are efficient so 2023 we 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 and it's a little bit different from being able to run something that you couldn't simulate because

6:36now 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 you know 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

7:09unachievable on a classical computer there'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 um 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 uh 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

7:41to be possible for a wide variety of users on our systems today and 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 problem 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 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

8:11back and forth between the quantum computers and classical computers to come to a bit of a close before we can really claim that yeah and quantum advantage is one of the confusing things because

Quantum Advantage Controversy

8:24google claimed the quantum advantage i think two or three years ago uh and and you know different companies i think mean different things by quantum advantage so in from ibm's point of view and and also you talked about the the number of qubits uh explain why the number of qubits is important and how many qubits are you working with at this point and and how do you create a qubit in your system because there

8:58are different kinds great questions i i'm sure you've heard the phrase there are lies darn lies and then there are benchmarks yeah yeah it's a little bit like that yeah um that one of the big questions on quantum advantage is verifiability yeah 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

9:35outputs that's hard to compute classically and they're able to show that sort of for smaller circuits the distribution had some measurable fidelity sometimes 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 some replicas of that that have kind of bigger differences between the quantum and

10:12the classical runtime at smaller circuit sizes where you really can't simulate it so maybe that claim has gotten stronger but again it's you know 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 in the open and it's actually

10:44started to get quite a few um events put on there is this a leaderboard that you guys publish or it's a leaderboard we initially set it up um but you just you make a um uh issue on github to add something to it it's not like we're um not like we're editing what goes into it that it comes from the community yeah uh and then on on the number of qubits and the kind of qubits and oh yeah so many questions there

11:15so um 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 that's really important that we have to begin and end with classical data because that's the

11:47only 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 this quantum device uh and specifically it's a vector space that has two to the number of qubits

12:18dimensions 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 um you can like i said just do it on a classical computer anyway if it's small like you know four qubits i could do on my phone quite trivially um 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 in the case of

12:50our 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 and there are a few dozen ways people do it um 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 you can point up or down and because that's a really weak

13:24magnetic field it's hard to see which direction the electron's 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

13:56photons and those are our two states and so keeping that hidden from the universe is a matter of making sure the electron 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 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

14:28to near absolute zero as opposed to a device that you put into your laptop over there so 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's the error rates that we can get you asked about device sizes currently our kind of flagship devices that we have in our fleet are 156 qubits to put that in perspective

15:00the google supremacy experiment you mentioned was done with 53 qubits around 100 qubits is work it's basically impossible to do classical simulation someplace between 50 or 100 so we're kind of significantly past that threshold we 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 qubits 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

15:32and 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 so it'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

16:07flip soar is enough sorry i don't usually do classical logic it's the same thing for quantum logic there's a big 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 all 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

16:38around um 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 um superconducting resonators so an lc circuit if that means anything to you um that basically a

17:13little patch on the chip that has a capacitor two pieces of metal that are close together and something called a jose injunction in between that acts like an inductor and so the energy alternates between electric field between those capacitors and phase basically current inside of the jose 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

17:48that 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

Expanding Qubit Numbers

18:02you have to build an array of now 156 qubits why is it is it sounds like an engineering problem but why is it so difficult to expand the number of qubits is it the noise problem the error correction problem there are a lot of problems that that's what makes it so much fun yeah so one thing to realize is although we're using semiconductor technology these qubits are actually pretty big um 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

18:36qubits with your naked eye i mean to me that's amazing like 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 um so some of the things that makes it really challenging is that we have a lot of io if you think about your microprocessor a bit of memory it has you know 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

19:09we 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 um on the order of 480 control signals going into and out of it and those have 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

19:46need 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

20:22house 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 so those are just a few of the engineering problems that you need to solve as you scale

20:36all 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 uh tunneling diodes and and when you bring it down close to absolute zero what you're saying is the the photon in that junction uh stops moving um so the junction works as long as you're

21:10colder than tc that as long as the superconductors are superconducting um for the materials we use uh colder than about one degree is enough um the reason why we need to be so much closer to absolute zero is actually thermal photons uh if i had an ir camera and i looked at right you right now i would see you were glowing yeah and uh 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

21:41not 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 joseph junction to work or even the qubit to work but just protecting it from infrared radiation i said infrared radiation i should have said heat radiation at microwave frequencies right um as for the photon um so we there's a lot of different types of superconducting qubits as well i should mention um that store the information in slightly different ways the description i gave you is appropriate for type of qubit that's called a

22:13transmon 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 uh what people call a phase current relationship that there's a relationship between um 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

22:46and that change phase is 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 jose injunctions but it's really voltages drive derivatives of currents if i put a voltage across it the current slowly goes up if i reverse the voltage the current slowly goes down um so in electrical engineering terms it looks like an inductor uh 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 um the cool thing about it is it's a ridiculously

23:17non-linear inductor um and the reason why that's important is you know i talked about zero photon and one photon uh 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 need really need a way to isolate that zero photon and one photon manifold and the way that we do that is the jose injunction because it is so ridiculously non-linear we can make our oscillator so that if it's if we're putting one photon into it that takes five gigahertz of photons but two the

23:48second 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's uh um that gets me there yeah i've never tried to explain this without a chalkboard and a lot of diagrams the uh the the the the issue to get so so you have those 156 qubits uh and you say that they're they're particularly useful for matrix multiplication is that well it's the operations

24:25that you go on them all have representatives in linear algebra um that most of our gates you can write as unitary transformations which are basically uh matrix multiplications that don't destroy any information that they have a inverse um a little bit more complicated than that but good enough for our purposes um 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 and my podcast is primarily about ai

Quantum Computing and AI

25:00how does that matrix matrix multiplication relate to ai uh i mean is there an application can will you be able to run uh artificial intelligence uh algorithms that depend on major matrix multiplication on a quantum computer so we 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

25:35and then there are things where you would need a very much larger um error corrected quantum computer we really should talk about our correction at some point uh to execute so for things that you can do today we need problems that map really efficiently onto the quantum computer and so you're looking mostly at things in chemistry and material science basically using the quantum computer to solve problems in quantum mechanics uh 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

26:05really getting close there um in this near-term period a lot of the time we're dealing with heuristic algorithms um 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 and 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

26:39represent 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 in 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

27:11what the periodicity of it is we can show that the quantum computer can win and you know again 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

27:29so 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 in 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

28:05yeah sorry i'm a scientist i sometimes talk that way uh there are some algorithms where i could sit down with a chalkboard and i could prove it to you right um that matrix multiplication i can sit down with a chalkboard i can write down the algorithm i can say this does it uh 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 uh quicksort another great example uh there are other algorithms uh like a beam search genetic algorithm

28:39radiant descent on neural networks where i can't prove to you that it's going to give you the right answer i 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 um 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 going to give you the answer you want you just sort of have to try and see um everyone's solutions to traveling sales but problem solving fall into this category as well um so um an example of you mentioned there are very few

29:17quantum algorithms an example of one of those blackboard algorithms is called shores algorithm yep and it's a pretty famous one because it's an algorithm for factoring numbers that's a problem that's believed to be hard for classical computers and actually um you mentioned um um oh my gosh i forgot his name scott aaron said earlier you know he has this sort of famous description of shore's algorithm of you know three things are possible uh one is that it's easy to factor numbers classically and somebody's going to get the fields metal another is that uh it's

29:52impossible 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 so short's algorithm is one that i can prove on a chalkboard um but it is incredibly hard to run nobody can build a quantum computer anywhere near close to be able to run shores algorithm on a usefully sized problem today and so it's one of those kind of distant future algorithms and so the other place where we could talk about ai and quantum

30:23just to circle back to where you came from is this distant future world that there are large chunks of linear algebra making some assumption that you're able to prepare the input states efficiently that you get speed ups with quantum computers on but the pre-factor the the cost of running it on a quantum computer because they're so much slower to start with is so large that even if you had a computer that could do it it would only be a winning situation for really absolutely enormously large problems

30:53we're a long ways off from that being possible and and that's because of the the number of stable qubits the number of qubits and the noise keeping us from running very deep circuits today there's a final algorithm people talk about called grover search i don't know if you've heard of this it's kind of it's a fun one because it's one that you can actually explain to somebody that knows a bit of linear algebra in a couple of hours how to do it and prove that it works and it does a weird thing it searches an unsorted list in a time that's the square root of the size

31:26of the list it's kind of okay that's a little bit weird like normally you have to search on about about half the list so if i gave you a list of 10 items you have to search about five of them with this algorithm you only have to search three and it sounds odd but if you have a problem where you it also works on functions of the list so if you have this list of a billion possibilities and you want to say give me the one that this function returns seven for i can do that and only search a square root of a billion as opposed to half of a billion and so if i had a huge infinitely large

32:00computer this is maybe really cool um but it's even further out there because again the pre-factor is so large that you're really just better off building a massively parallel classical computer and doing it that way and and this is another one of these places where i think people get really confused because it's an easy algorithm to explore and so they think it's going to be important technologically but really it's just so off so far off there it's unlikely to matter in either of our lifetimes the the the challenges that everybody's working with is noise isolating from noise and noise

32:37is can be many things it's not sound waves right uh and and uh and the other is uh error correction the noise issue i think everybody can conceptually understand uh error correction can you talk about

Error Correction in Quantum Computing

32:56that absolutely um so to circle back to our roadmap this is something we think is going to kind of come into the world as far as things that are useful for other people to use as opposed to research projects kind of the 2029 time frame so it's not that far away and the game is pretty simple that it shows maybe it's best start with kind of classical error correction which maybe not everyone's familiar with it gets used a lot in communication um imagine i'm trying to send you some data but every now and again my radio receiver or your my transmitter or your receiver makes a mistake

33:29and so i want to send you a one i can do something really simple i could send you three copies of that one and then you'd look at them and if you saw one one one you know it's a one if you saw one one zero you'd say well probably it was a one right and so you can correct some errors that way there are more complicated error correction schemes set on based on for example parity checks so imagine i was trying to send you eight bits i could send you those eight bits and then i could also send you whether there was an even or an odd number of ones in them and that would be enough information that when you got those eight bits provided there was only one mistake you would be

34:03able to say this is correct or this is incorrect if i added two parity bits you would actually be able to correct the mistake or you'd be able to detect larger numbers of errors right so i can build different codes that protect different amounts of data with parity checks the more parity checks i add the more mistakes i can correct and um you know the better the code works if i had to send some extra data and there is a huge theory of how to do classical error correcting codes because communications is so valuable um it's very advanced like error correcting codes are actually built into the uh

34:36i go back to the cell phone the radio transmitter and receivers in your cell phones we can also use error correcting codes on quantum computers it's just it's a little bit trickier because if you look at the qubits you destroy the quantum state and so if i do that simple repetition code we started with where i send you three qubits you've got to look at them and say well are they all do they all match that doesn't work but those codes based on parity checks can actually work and the reason is i can design circuits that will tell me the parity of a set of qubits without actually telling me what any of the qubits were and so i can kind of do a blind error correction that way

35:12the tricky thing about this is we have two types of errors we need to correct in quantum computers what we call bit flip errors one goes to zero and what we call a phase error which is where super positions come out at the wrong angle think of it as a timing error um and so we need to to correct one qubit we need to use two error correcting codes and i need to use enough of these parity checks that i can get to error correction as opposed to error detection and our error rates apply to all the operations including the ones that we use to prepare and measure the parity checks so if the error rates

35:45are too high to start with the code actually does more harm than good like it actually damages the information as opposed to correcting it so you put all these things together and error correction is actually pretty challenging when people talk about error correcting codes that have enough protection for me to run some of those long distance of those far future algorithms they get answers that we need error rates that are better than what we have today if you look at our best processors kind of three to ten times better so not you know miles off but better but the really big problem is with the error correcting

36:17codes people understood until recently the overheads in terms of number of qubits were huge it would be pretty typical to talk about using 100 physical qubits to make one error corrected logical qubit so we're saying we could take our best processor today and we can make one logical qubit out of it that was good enough for a far future system right and that's why people talk about logical qubits now yeah and so that logical qubit would be 100 physical qubits plus the error correction apparatus around it so if you

36:47think about that it just really pushes the system size and cost if you're using a code like that to something that we felt was kind of beyond the reach of engineering um last year that changed and the reason why it changed is a little bit subtle people have also known about you know as i said this is a really well studied problem from communication classes of codes called low density parity check codes that in principle could have much lower overhead many fewer additional physical qubits to protect the same number of logical qubits it's just there was a dearth of kind of practical

37:22implementations that people knew that they could map to hardware where people knew exactly how to do the center extraction where people that measure the parity checks where people knew how to determine from the parity checks what the years were to start with and we were able to fill out the details of one of those codes that we're calling the gross code and show an error correcting code that's an order of magnitude more efficient than anyone ever knew was possible before now that didn't come for free but the reason it didn't come for free i actually think is really fun because i told you i'm a hardware guy i just love to

37:53make you know make things that work um the earlier error corrected code error correcting codes um most spot though was called the surface code only required nearest neighbor connections between the qubits remember i said there were wires that connect qubits we used to run operations and so you could use qubits laid out like on a checkerboard and implement that code to get these high rate codes to work you need non-local connections you can't do them in two dimensions anymore you need a way to pop out of the plane and kind of do a highway overpass and so the really great thing about the gross code is we

38:23were able to work with our error correction team here and kind of tell them well we think we could make a chip maybe we could have six predictions per qubit we think we could get the wires to go about 10 qubits away and still work and they're able to design an error correcting code specifically around the constraints of what we thought we could build on the chip and so that kind of that co-design is where this really came from and so the other really exciting thing that happened this year is well end of 2025 we're now testing in 2026 is showing all the features that we need to implement that air

38:54correcting code on a single chip and it's a really experimental one it's not one that we're going to make available to clients over the cloud because at the end of the day it implements two logical qubits not very useful but it shows all these neat features and it's really the prototype for these systems that we're going to be trying to build in 2029 2030 2031 that let us tackle these big chalkboard problems um and when you talk about 156 cubits you're talking about physical cubits not logic logical cubits i mean not arrays of yeah yeah uh the um how much of the work is

39:33is on software in software i mean working out solutions using code how much of it is as you just described designing new configurations of hardware and the new configurations of hardware hardware sounds like a very expensive process uh and is that done here i mean for example the quantum

40:09computers i've seen here uh are you how how fixed is that hardware are you constantly changing it that we have a huge research effort changing improving that hardware both from the design viewpoint the materials that we put into them how we fabricate them it's a big chunk of what we do our team is probably about one-third focused on the hardware and two-thirds focused on the software which kind

40:39of gets a little bit to your first question um that tackling the hardware part first because that's just the guy that i am uh until a couple of years ago every quantum processor we built was built in this building we have a small research fabrication facility here it's small by comparison to like a samsung or a tsmc or an intel that we're really able to get this entire thing off the ground recently we've moved all of our fabrication up to the most of our fabrication i should say up to the albany nanotechnology

41:12center which is a state-of-the-art 300 millimeter again research pilot production facility not you know full-on production line that's co-run by ibm and ny creates and the reason why we did that is our processors are getting more complicated with these extra layers they run 24 7 they have modern more automated tools and so it was able to build these more complicated devices in the same amount of time which is great

41:39the finishing packaging of the devices is still done here for everything else a lot of it comes from elsewhere that you know the control electronics it's designed by the same people that design system z but at the end of the day it's built by subcontractors and then it all comes together here or at our site up in poughkeepsie new york for assembly and test and and those uh quantum computers that i've seen you you have uh them in various places around the world for various research institutes to

42:12work with when you change things here and and you like what it does do you then go out and change all those other yeah so we computers we have sort of two classes of client accessible quantum computers one is we have our own data centers which is where people can have cloud access to our machines you actually get 10 minutes for free every month so if you want to go try it you can and for those we kind of upgrade them when it makes sense to us typically they'll be running for about two years

42:46before we upgrade them for the ones that you're talking about in other people's buildings i think there are 13 of those right now around the world and for four more getting installed towards the end of this year it's actually a pretty big fleet those are actually managed service agreements so we still own the quantum computers that we install them at the customer site we manage it we keep it calibrated we keep it running they get to use it to run whatever they want to on it those typically include a mid-term upgrade after about two years with the expectation that this is a technology that's

43:21moving so quickly that by two years out you're really going to want the latest and greatest anyway and that's something you know we coordinate with when we actually have something that's appreciably better

43:33we turn these devices around pretty quickly you heard me spew out about a half dozen bird names um but you know we're working on three different versions of that nighthawk processor right now and whichever ones look good that'll be the next one um so it changes incredibly quickly from a hardware perspective uh so on the timeline quantum advantage a verifiable quantum advantage of this year or i should say uh uh uh repeatable quantum advantage right so verifiable if you go look at the tracker arguably

44:11we could have a discussion maybe it's already there it's close yeah and and what what's the next milestone uh you know without going out 10 years so we have um two roadmaps we have a development roadmap which is about devices that we think are computationally interesting that will go out to our clients and for the next three years so 26 27 28 what's on that development roadmap are improved versions of uh nighthawk which is our current state-of-the-art processor uh improved in the sense

44:43that the gate fidelities will get better so fewer errors and that's really that materials and physics research uh and larger versions of them and that's really that engineering and system scaling and so these are going to get continuously more and more capable on the background of that we have a lot of work on how to use them better using techniques like error mitigation or even smaller versions of error correction that don't give you large-scale fault tolerance but can fix some small errors fix some specific errors getting put into the circuits people run so they can accomplish more and more

45:14and we try to capture that with a single number which we call the number of gates that the device can run the number of operations it can run um in the develop in the other roadmap is the innovation roadmap and that's the one where we capture the uh research demos the things like these air corrected devices that we don't think are computationally useful yet and so over the next three years this year we're going to be demonstrating logical qubits next year we're going to be demonstrating connecting two modules of logical qubits together because to get the system size that we want to build we can't

45:45do it as one huge monolithic chip and then the year after we're going to be demonstrating universal computation on these guys and at that point in 2029 we are going to be hopefully bringing those error corrected devices to clients and these two roadmaps kind of merge again so i i have to say um you know i've been at ibm for over a decade now i've been doing quantum computing for much longer than that i used to think it was really a question of am i going to see an air corrected quantum computer in my lifetime and now to be saying actually we think we're going to be building one that's big enough to be

46:18computationally useful in the next four years is both amazing and terrifying but it's really these new more efficient air correction codes have rewritten what's possible um in the next decade the uh you know i was asked to do some writing for different clients i was asked to write a piece about now is the time uh that that enterprises should start training a cohort of people so that they understand quantum

46:53algorithms uh and they understand the the timeline so that when it arrives they'll have a some workforce or subset of the workforce that's ready do you think that's too early i don't think so um from a couple of perspectives um one is that right now is the time when you really should be figuring out what algorithms that you want to use because everyone has their own problems that they want to solve and how to map that even in the abstract sense to a quantum computer whether you're using one

47:28of these heuristic algorithms or a blackboard thing that you can run in a decade or four years it's a big challenge and finding people that understand both your problem and quantum computing well enough to do that mapping it takes time um the other thing is i kind of like to say if i could have told you four years in advance system 360 is coming out and you could have been you know ready and raring to go on the day that that thing was available that's a huge advantage and so kind of from both of

47:58these perspectives if you're a large enterprise that's investing on you know this four and five year horizon i think it is absolutely the right time to be thinking about it the other thing to keep in mind is we do have these near-term heuristic algorithms where we're getting really close on optimization we're really getting close on chemistry where it seems like we may well cross some additional thresholds in the next couple of years i can't promise it they're heuristic but it's very encouraging um and and final question uh a minute ago you said uh you know in four years uh that's kind of

48:36terrifying is it terrifying because you now have this goal and it's you know are we going to get there or or or make the the deadline so to speak or is it terrifying because of the power of quantum computing and how it's going to impact the world i think the impacts are going to be overwhelmingly positive actually um that you know we name the ages of man after materials and so to say that i have a computer here that's going to help material science it is you know how can that not be a

49:07wonderful thing uh for me it's terrifying um we talk a lot here about cycles of learning you know i'm going to build this device and from it i'm going to learn the things i need to do the next one and from that i'm going to do four years is short enough that i can now count the numbers of cycles of learning that we have left before we have to deliver that device and although it's possible um it's honestly it's going to require a lot of very hard work and we know things are going to go wrong in the meantime and so it's the concern is more about timeline about are we going to hit that 2029 year

49:42or too many things going to go along we're going to slip out a year as i said we put a lot of pressure on ourselves to stick to that roadmap yeah and so the sense in which it's most terrifying to me is yeah keeping on that schedule like doing science on a schedule is a tricky thing that's right okay oliver well thanks very much that was fascinating and i'm sure will be fascinating to listeners there's a lot of information there and i encourage people to run a transcript and run it through chat gpt if there are things that you don't understand uh so great i i hope we talk in four years and and you can

50:23tell us how things have advanced well i hope i see you back in the building again sometime before that and maybe we can take you around some of the labs let's just let you absolutely love to yeah yeah so and yeah it's been an absolute pleasure and um yeah maybe next time you could also tell me how we can use ai to make this all go just a little bit faster yeah okay take some of the tear out okay great thanks thanks thanks