In this episode of C&EN Uncovered, host Craig Bettenhausen speaks with C&EN assistant editor Ananya Palivela about recent advances in quantum computing. Ananya and Craig talk about the results of experiments that place quantum computing, classical computation, and empirical data head to head, as well as what the results mean for the coming advances in computational chemistry. You can find the link to the article here.
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Executive producer: David Anderson
Host: Craig Bettenhausen
Reporter: Ananya Palivela
Video + Audio Producer: David Anderson, Jeremy Barr
Episode artwork: Oak Ridge National Laboratory
Music: Commercial Flow, Shutterstock
Contact Stereo Chemistry: Contact us on social media at @cenmag, or email [email protected].
The following is a transcript of the episode. Interviews have been edited for length and clarity.
Craig Bettenhausen: Welcome to C&EN Uncovered. I’m Craig Bettenhausen. C&EN Uncovered is a podcast series from Stereo Chemistry. Each episode we’ll take another look at a recent story in chemical and engineering news and hear from C&EN reporters about striking moments from their reporting, their biggest takeaways, and what got left on the cutting-room floor.
This episode, we dive deeper into quantum supercomputing—specifically a recent benchmark reached by a computing world’s superpower. In collaboration with multiple universities and national laboratories, IBM recently started benchmarking its quantum computed calculations against real-world experimental results. We’re here with C&EN reporter Ananya Palivela, who wrote about the work at the end of March. We’ll put a link in the show notes along with episode credits.
Hi, Ananya.
Ananya Palivela: Hi.
Craig: So for those that haven’t yet had a chance to read the article, can you give a brief recap of what it’s about?
Ananya: Yeah. So, recently, a quantum computer successfully simulated a real experiment and showed that the results match what happened in the lab. and it demonstrates that these computers actually work.
Craig: So we don’t always go all the way back to the basics on this show, but as I’ve been chatting about this episode, people of all stripes have stopped me with this question I’m going to ask you: What is quantum computing?
Ananya: So I think explaining a quantum computer, it would help to start with how a simulation works. Simulation is just a computer-generated stand-in for reality, like pilots on a flight simulator or F1 racers on a car simulator—so trying to simulate experiments to see how they would go without having to spend the resources of actually having to do the experiment.
The simulations help scientists understand how things work on a fundamental level. It’s like watching a movie of the experiment take place, but it allows them to switch out the materials or change the reaction speeds or temperatures. This lets them probe scenarios by changing one variable at a time or exploring conditions that might be too difficult to create in a lab.
So the thing with car simulations is that they’re made on an ordinary classical computer. But for experiments—especially those involving materials and chemicals—the results depend on the way molecules and electrons interact with each other. And the way these interact with each other is really bizarre, and it’s what is known as quantum mechanics.
So when two classical objects, like two balls, collide, they just bounce off of each other, and you would know how they would bounce off of each other. But when two subatomic particles collide, they scatter in way more complicated ways. And sometimes they create a whole new particle, or sometimes they just disappear into nothing. And basically predicting what would happen or simulating it becomes a lot harder. And with every new electron or atom introduced into the experiment, the problem becomes that much harder. It’s almost like if you would think adding a new ball to the collision experiment would be like opening a new tab, maybe adding a new electron would be like opening 10 tabs, and then adding the next electron would be multiplying that instead of just adding more. It would make your computer crash.
So as the number of atoms and electrons becomes larger, the number of particles that make up these chemicals and materials, these calculations become impossible for a classical computer to solve. So even with all the time and computational power in the world, some of these problems would still be unsolvable. Things just like how light is hitting a hydrogen atom or something like that would still be really hard for a classical computer to solve, and that’s where a quantum computer comes in.
So instead of calculating what would happen in the experiment, a quantum computer becomes the experiment. Since both the real world and a quantum computer function on the same rules; they both use quantum mechanics. A quantum computer can mirror reality exactly, while a classical computer is just mimicking it and approximating it all the time. So that’s where a quantum computer should win out. And for the first time, the scientists show that with a quantum computer actually mirrors a real experiment.
Craig: Interesting. Since C&EN’s been writing about quantum stuff, we’ve had the quantum realm come out in Marvel movies. I’m curious, when you talk to people, do they avoid saying the quantum realm or do they embrace that new pop culture relevancy?
Ananya: Honestly, I don’t know that the scientists know that Marvel has used this.
Craig: Interesting.
Ananya: . . . word. I think a lot of people say the quantum realm in general, because I used to say it a lot before the Marvel movies came out.
Craig: Yeah.
Ananya: But I’m pretty sure there’s very different definitions of what that means to the movie-makers and the physicists.
Craig: One hopes so anyway.
How did these systems pass information from the quantum realm back out to the sort of, well, the binary data that the computers that we have to interact with?
Ananya: So one of the major limitations of this paper and in general with quantum computers also is that a lot of the post- and pre-processing steps are done on a classical computer. So data is fed into it and a ground state, a quantum ground state, is prepared in a classical computer and then it’s fed into a quantum computer. And then a quantum computer then does some part of the calculations on this quantum data, is basically like measuring an experiment. So when you’re measuring a real experiment, you are converting real data into numbers on a computer. So that’s kind of what’s happening here. So it’s kind of like you’re measuring an experiment, but you’re measuring what’s coming out of a quantum computer.
Craig: Interesting. So you explain this a little bit. I’m going to ask you from another angle though, but what is the advantage of a quantum computer compared with a normal supercomputer?
Ananya: So actually I was talking to Alessandro Curioni, one of the VPs at IBM Research, and he really emphasizes on the fact that a lot of people believe that a quantum computer is just a faster processor or something that would be more efficient. But I think what’s important is that it’s not just a faster supercomputer but it’s a computer that can do something that a classical computer would never be able to do. So that’s what people call the quantum advantage. And this is very highly debated and a lot of people disagree on what it really means. But most people agree that a quantum advantage would be when a quantum computer is able to do something that a classical computer isn’t able to do. And this doesn’t necessarily have to be in terms of speed. It would most likely be something about a problem that’s not solvable on a classical computer.
Craig: Can you give an example or two of what these problems that classical computing can’t solve that quantum might be able to, what those would be?
Ananya: Yeah. So even this neutron scattering experiment, for example, is a problem that people do solve on a classical computer, but it’s solved with multiple approximations. And while these approximations are slowly becoming more and more accurate, it’s impossible for them to ever be fully accurate because any experiment really that’s happening in the real world—or not even experiment, anything happening in the real world is truly a quantum phenomenon. Nothing is really classical. And sometimes this doesn’t matter because we’re not going into the most detailed version of that experiment. If you’re just kind of looking at what’s happening on top, then it doesn’t really matter what’s going on on the inside sometimes. That’s why quantum computers solve problems that classical computers can’t because they’re quantum themselves.
Craig: So the data at the heart of this particular advance is on neutron scattering by a potassium copper fluoride.
Ananya: Yep.
Craig: What is that and why do we care what it does to neutrons?
Ananya: So honestly, we don’t. The material used here is more of a benchmarking material. The only reason it was used here was because this experiment has already been simulated classically. And also another thing that this experiment shows is that the classical computer does the whole simulation way better than the quantum computer does in terms of both efficiency and accuracy. The classical simulation is just better. And also, like I said, the pre- and post-processing steps are all classical. And what actually happened in the experiment is that the computer uses a classical simulation to calculate the whole experiment, and then it goes backward and breaks the calculation into easier steps and feeds these new approximated steps into the quantum computer. So technically it would be impossible to do without the classical computer.
Overall, there is no advantage here clearly because a classical computer does everything better and most of the work is done on a classical computer. The main idea of why the experiment is important is just because it’s kind of like you need to show that your tools work before using them for anything real. And right now, quantum computers are super noisy and error prone and proving that even with all of those limitations, you can still get a quantum computer to actually produce accurate results on a meaningful experiment and match real data is like a great benchmark. Neutron scattering experiments in general are used to help understand how the internal dynamics of a material works so that you can understand how the material would do things like conduct electricity.
Craig: X-ray scattering, a lot of chemists will be familiar with. It’s a common technique. You can fit that instrument into a room about the size of four porta-potties.
Ananya: Yeah.
Craig: But neutron scattering is far more complex. You have some photos in the story of this large apparatus. Can you talk a little bit about what that looks like?
Ananya: Yeah, so it’s a giant apparatus and there’s like in the Large Hadron Collider, there’s a lot of really fast-moving particles colliding. So a bunch of neutrons are thrown against the material, and people look at how the neutrons scatter off and where they land around the material. And that helps them understand how the material works or what’s happening inside, because really fast neutrons penetrate materials better than most other things do.
Craig: Now the quantum computers themselves are also alien looking. Can you talk a little bit about what those look like?
Ananya: So the processor part of it is probably really, really small. The whatever you see the extended-out exterior that looks metallic and cube-like is a lot of cooling technology. Basically the biggest problem with quantum computers is that they have to be really isolated and kept at really, really, really low temperatures. And this is one of the reasons why it’s so hard to build them and scale them into bigger, actually useful computers. So whatever we see is mostly the cooling technology that’s being used to cool down and isolate the state.
Craig: I know that quantum computers have to be mind-bendingly cold. Is that a temporary limitation that might lift with enough R&D, kind of like we’re seeing with high-temperature superconductors? Or will quantum chips always have to be close to absolute zero?
Ananya: So anything can happen. Yeah, so there are researchers working on slightly higher temperatures of qubits, but most researchers agree that we’re going to stay in the same range that we are right now. And while there are talks about efficiency, it would be to maybe increase the efficiency of the cooling technology and things around that rather than actually trying to change the temperature range where this would work. And even if it does change, I wouldn’t expect it to change more than a few kelvin, which would still mean they would require these large cooling technology surrounding them.
Craig: Interesting. Chemical & Engineering News has been covering quantum computing for a number of years now. And in fact, there’s an Inflection Point episode, our other podcast, that’s out. It talks about the history of the tech and what qubits are and supercooling and even gets into the Schrödinger’s cat that you referenced. So I’d encourage listeners to check that out to dive into another wing of quantum computing.
So we talked about it a little bit, but spoiler alert, how did the quantum computer stack up? How did it do, the idea to test it?
Ananya: Oh, it did pretty great. Honestly, if you look at the data they show, it looks like the quantum computer really matched the lab data better than the classical computer did. But the authors of the paper at least point out that that was a coincidence and that the noise in the quantum computer ended up matching the noise in the real experiment. But it’s not really showing any underlying physics. But they show that it does match up quite well, and you can see that in the paper.
Craig: Oh, it’s just also noisy, but for a different reason?
Ananya: Yes. And we would prefer if it was noisy for the same reason.
Craig: That’s fair. Yeah. Interesting. I ran into that in some of the photo stuff I’ve been nerding out on where film is noisy and digital image sensors are noisy and it looks very similar, but it’s for completely different physics reasons. I always wonder if that’s like a human design question or if that’s just some inherent nature of the devices themselves.
So a researcher quoted in your article says the differences between the quantum computing simulation and reality are negligible, but we’re talking about very small things, and small differences matter. Do they imagine there will ever be a perfect one to one?
Ananya: I guess so. I mean, I don’t know if that’s what they’re looking for. I don’t know if they want to exactly match a real experiment. Maybe they want something that’s even less noisy or more clear. The experiment they did was at 6 K, but they would’ve preferred to have done it at 0 K, but they can’t because we don’t get there. Or even lower temperatures, maybe not 0. But with a quantum computer, you could do that because it’s just a simulation. You could make it lower temperatures.
Craig: Oh, I see.
Ananya: So you could make more ideal versions of experiments, something that you couldn’t even do in a real lab. So maybe that’s the true advantage of using a quantum computer: you’re able to do experiments that you wouldn’t be able to do in a lab.
Craig: And I guess that idea of swapping out atoms casually was really intriguing because I know there’s a lot of interesting work on high-temperature superconductors actually. But trying to change the crystal structure, change it a little bit in a way that’s very, very difficult at the bench and could be a lot—well, it’d be worth doing it 100 times in a simulation because the synthesis is so very hard.
So how far are we from quantum computing being a real practical set of methods?
Ananya: That’s a real question, isn’t it?
Well, if you ask most researchers at places where quantum computing’s happening with Google or IBM, they’d say, “Oh, we’re here. Quantum advantage is either that we’ve already reached it or that we’re going to reach it by the end of this year,” or something like that. And while that may be true in their sense of their particular definition of what quantum advantage is, the question that we really want to ask is, When are these computers going to be useful? And if you ask a question like that, then most researchers are usually confused and don’t really have an answer.
But there’s a paper in 2023 that showed that an IBM quantum computer was able to fully simulate a certain experiment. They didn’t match it to any real experiment, they just simulated an experiment. But very soon after it published, Garnet Chan, a theoretical chemist at Caltech [California Institute of Technology], he and other people showed that they could solve the exact same problem. So the paper was titled “True Utility of a Quantum Computer,” or something like that, and then they showed that they could solve the same problem on a standard laptop.
And there have been many such instances of quantum computing with controversy. And there’s discourse among researchers and journalists talking about when we might see quantum computing that’s truly useful—the computers that may actually help us design new materials or do the things I was talking about; most researchers agree is that they’re all still a long way away. But with tech giants funding this research, I feel like some scientists and many other people that I’ve talked to also agree that they’re forced to make the scenario seem better than it is.
I think there was a Frank Verstraete study, a physicist at Cambridge, and he was saying in an interview that a quantum computer only exists if you have a fault-tolerant quantum computer—everything else is not even a quantum computer.
So yeah, today the computers are very noisy. They’re prone to many errors, and they may work on small experiments that don’t matter. But scaling them to the millions of qubits that we require to solve the problems that the computers are being built for would mean that we would need to get rid of these errors. So we’d need better hardware.
John Martinis, the 2025 Nobel Prize winner, I was talking to him and he was telling me how important this is today. He says that we need chemical engineers and materials scientists around the world to solve this problem. We need better quantum materials; we need better ways to synthesize the quantum materials and better ways to put them together. And all of these are fundamentally materials science and engineering problems, and it’s all the hardware problems. So this isn’t an easy problem to solve.
Craig: Yeah, that’s an interesting question because I know that within a classical computer a bit is a little magnet, like literally a small magnet that’s being flipped back and forth. What is physically, materials science–wise, what is a qubit made of?
Ananya: So there are many different qubits, but the one used most often is called a superconducting qubit, which is a circuit made of a superconductor with something known as a Josephson junction in the center of it. This is the science for which John Martinis and the two other people won the Nobel Prize last year. So this junction allows electrons to sometimes pass through and sometimes not pass through. It’s called tunneling. It’s like there’s a hill and there’s a ball on one side and suddenly the ball disappears and appears on the other side without going over the hill.
Craig: It’s a good trick.
Ananya: So that’s what a Josephson junction is doing. There’s like a barrier, which isn’t a conductor. There’s an insulating barrier and two superconductors on two sides, and the electrons are able to move from one side to the other, jump over the insulator. And this creates what is known as a system with two levels that are not linear.
Craig: OK.
Ananya: And it basically allows for two states to exist there. And since it’s a quantum system, since the phenomenon is tunneling, it exists in both states until observed.
Craig: Interesting.
Ananya: Yeah. Honestly, I tried to really, really, really make it as simple as possible. It’s not exactly that, but it’s like a switch. A bit is on or off, a qubit is both on and off at the same time, and it allows you to compute all the possibilities at once instead of one by one.
Craig: Yeah. I always enjoy thinking about quantum stuff, but then still walk away confused. But I find some comfort that when I’ve talked to other people that do quantum work, they said, “No, you should be still confused. We’re all still confused.”
Ananya: Yeah. I mean, I’m not a physicist either.
Craig: Right.
Ananya: I’m used to talking to researchers and completely blanking out for a bit and just being like, “What?” And they’re like, “Yeah, I know.” And I’m like, “You do this work.”
But yeah, as I said, it isn’t an easy problem to solve, but the development of the quantum computers over the past few years is what most researchers would call impressive. And it has been going faster than at least the academics expected it to go. And building these machines is also helping scientists understand how our world works and uncovering science that we haven’t tested and observed before. It’s like the space race, right? Just trying to do something helps us understand some other things and people point out that real science doesn’t happen overnight, so . . .
Craig: True. Yeah. I love the idea of them figuring out that they can do something with classical computers just because they were told they couldn’t. That’s a cool concept.
I mean, similarly, does AI [artificial intelligence] change any of those sort of dynamics because you said AI is doing some things that we thought classical computers couldn’t. So how does that play into it? Is that coming and changing the questions people were trying to ask because they now have this third computational tool?
Ananya: I think AI is still a classical computer at its core, so they’re very different things. And yes, definitely while AI is able to solve problems that maybe supercomputers couldn’t, I think this is different problems. I don’t know a lot about AI, but people definitely expect that AI would be a tool that’s used along with quantum computers to get useful results. So the pre- and post-processing steps or anything that’s done on a classical computer maybe be done by AI. And maybe do the whole materials discovery phase of it, which is something that people are using AI for would include quantum computing in its workflow to better find materials.
Craig: Interesting.
Ananya: I think that’s what people were talking about at the ACS [American Chemical Society] meeting last year.
Craig: Oh yeah?
Ananya: About using AI mixed with quantum computing and that would truly be the way to design new materials or chemicals.
Craig: That would be wild. I’ve still got to wrap my head around AI. I mean, everybody does, but I’m supposed to write an article about it by the end of the month, is what I mean.
Ananya: I’m supposed to write an article about it by the end of the month as well, so . . .
Craig: We should compare notes. So it’s an important result, but as you said, it’s not groundbreaking; quantum computing is not galloping ahead of regular computing. But what do they build on from this? What steps do they take next?
Ananya: I think primarily this is about establishing a workflow. It’s about showing that where things are and measuring what’s happening, and now they know. And I think then it isn’t a single set of steps that has to take place. It’s like a collaboration of multiple people. Like I said, there’s material scientists and chemists or engineers on one side trying to build better hardware and there’s computer scientists on the other side building better algorithms, and it’s a lot. But I think overall it would be that we need to scale and build fault-tolerant computers and that would require just better engineering and better materials.
Craig: Now what does it mean for it to be fault tolerant?
Ananya: It means that the qubits inside the quantum computer are supposed to be in this on/off state, but sometimes they interact with their surroundings and stay locked in one state.
Craig: OK.
Ananya: And then they’re not able to process information and they lose the information that they had. It’s called qubit decoherence, which is what happens when a qubit loses the information it has. And this is the biggest problem in quantum computing. And for the qubits to not be able to lose the information, they need to build better qubits and better isolation systems. Oh yeah, there’s that as well. The cooling system and the way it’s isolated in the vacuum chamber and all of that also has to keep being improved. It’s just research in every direction that would help them be better.
Craig: That’s exciting then. Lots of room to work.
So, this sounds expensive. Who’s paying for it?
Ananya: What’s surprising is that I personally, I wouldn’t know if the people at the top of Google and IBM really know what quantum computing is to be able to fund it like this. I don’t know if they believe that there are going to be applications in the near future for them to show off or if they truly believe that the science is worth pursuing. So there are a lot of tech giants and there are a lot of quantum computing start-ups that are working on building these computers. And I know that most of them believe that they’ll be able to reach truly useful computing in the next few years. But in the end, at least for someone like me, I believe that the science is just worth pursuing it, but the funding is definitely coming from all of these tech giants. Well, I mean, if they lose money, I guess that’s good for all of us, right?
Craig: I think it feels like they have some capital they can use that they’ve done all right over the past 20 years.
How did this topic come to your attention?
Ananya: I’ve always been into quantum computing and how they work and trying to understand more about them. And yeah, I saw that the paper was published, and I’ve been looking into other quantum computing papers. And there have honestly been a few papers published before this one that they’re able to simulate experiments a little bit better and better. And in this one they showed that they were able to make a real experiment. It’s been a series of small steps. I wouldn’t say that this was suddenly a huge breakthrough. This paper finally seemed like something worth reporting on.
Craig: Fair enough. So like you said you’ve been watching this for a while, is there anything that you found in reporting this that didn’t fit into the story but that you found captivating?
Ananya: I think it was just the controversy of it all that there’s been a lot of research happening and all the researchers I talk to are still very excited about this, and whether they’re in academia or in a big company.
And I would feel that for a big part, so much of the online discourse or everything that’s happening is based off of press releases and based off of what’s on the outside and not what’s inside the paper. If you actually read this paper, then there’s a lot of things that aren’t in the press release and it doesn’t claim anything more than what it is. It’s always like the press release that does. And I feel like maybe it’s the people that are around quantum computing rather than the real scientists that are forwarding this idea that this is going to be something that it isn’t. While if you talk to the scientists and the people that are working in it, while they’re really excited about what’s happening and they believe that whatever they’re working on is cool and it’s impressive, whatever they’ve been doing so far, you’d get the idea of how far it’s come and how long there is to go. And most researchers believe for this to be useful, it’s going to take awhile.
Craig: Yeah, I can never tell if I would also like being on the PR [public relations] side instead of on the journalism side.
Ananya: It’s fun. It’s fun to say, “Oh, this is amazing.”
Craig: Yeah.
Ananya: This is going to be the next best thing in the world. I’d like writing that.
Craig: Yeah.
Well, Ananya, thank you for diving deep on this with us.
Ananya: Yeah.
Craig: So people can find me on social media as @CraigOfWaffles. And Ananya, how can listeners get in touch with you?
Ananya: I am on BlueSky, @AnanyaPalivela, and I’m on LinkedIn on Ananya Palivela as well. Yeah.
Craig: You can find Ananya’s story about this quantum computing milestone on C&EN’s website. We put a link in the show notes along with the episode credits. We’d love to know what you think of C&EN Uncovered. You can share your feedback with us by emailing [email protected].
This has been C&EN Uncovered, a series from C&EN Stereochemistry. Chemical & Engineering News is an independent news outlet published by the American Chemical Society.
Thanks for listening.
