Best of: The future of ultrafast electronics

19 Sep 2025 · 36 min · 13 chapters

Ask about this episode

Ask anything about it. ChatGPT or Claude reads this page and answers with the times it was said.

Connect VO and ask about every podcast you hear, including the moments you saved. Add to ChatGPT · Add to Claude

In short

Re-shared episode on the “future of ultrafast electronics,” explaining how attosecond (10^-18 s) X-ray pulses enable “slow-mo” movies of electrons and chemical/quantum processes, and how that could drive faster electronics, quantum computing, and medical/material advances. It also covers how AI/ML helps run and interpret next-generation X-ray free-electron laser experiments.

Guest backgrounds

Physicist Matthias Kling, Professor of Photon Science and Applied Physics at Stanford University; expert in ultra-fast pulses of light and their applications.

Key claims

Attosecond X-ray pulses let researchers measure electron emission timing (photoemission) and probe atom-specific dynamics in complex systems. “Light-wave electronics” could switch devices on petahertz timescales by using intense light fields to control conductivity. XFELs can image biomolecules at room temperature in single shots, enabling studies like Photosystem II. AI helps with experiment logbooks, accelerator alignment, and real-time analysis of massive data streams.

Notable examples

Nobel-winning high harmonic generation (2001) producing attosecond pulses; photoemission timing up to ~700 attoseconds; using tuned X-rays to target specific atoms (e.g., nitrogen/oxygen) in large molecules; molecular “fingerprinting” via blood; XFEL single-shot diffraction imaging; Photosystem II oxygen generation from water.

Written by AI. May contain mistakes. Listen to the episode to check what was said.

Chapters

Tap a time to open that second in VO

Understanding Ultrafast Electronics

0:46 to 1:58

Discussion of the advancements in ultrafast electronics and their implications.

“That'll guarantee that you never miss the future of anything.”

The Science of Attoseconds

1:58 to 3:56

Exploration of the concept of attoseconds and their significance in physics.

“Well, Matthias Kling, as a professor of photon science and applied physics at Stanford University and an expert at these very fast pulses of light and their applications.”

Historic Innovations in Photonics

3:56 to 5:50

Discussion of the historical breakthroughs in measuring attoseconds.

“A-T-T-O second is what we're talking about here.”

Capturing Molecular Motion

5:50 to 8:12

Explaining how ultrafast pulses can capture rapid molecular movements.

“Atto seconds are our new favorite time unit.”

The Electron Emission Process

8:12 to 10:10

Insights into the photoelectric effect and electron dynamics.

“The photoelectric effect is something that Einstein essentially was able to describe using quantum mechanics.”

Future Applications and Theoretical Implications

10:10 to 14:00

Possible future advancements in materials science and medical applications.

“Six years ago, we took six years for the theory to catch up.”

Advancements in Autosecond Science

14:00 to 21:31

Learn how autosecond science is shaping medical applications and diagnostics through light measurement techniques.

“So this is definitely true for autosecond science.”

Introduction to the Next Segment

21:31 to 21:53

The host introduces the next discussion on ultrafast electronics and its implications.

“This is the future of everything with Russ Altman.”

Exploring Ultrafast Electronics

21:53 to 28:01

Discover how ultrafast electronics may revolutionize computing and the role of light waves in speeding up electronics.

“They're making movies that are super, super slow-mo.”

The Promise of Room Temperature Quantum Computing

28:01 to 29:29

Explore the potential of quantum computing at room temperature and its efficiency benefits.

“that's how we call it, or petahertz electronics, is also doing.”
Show all 13 chapters

AI's Impact on Experimental Science

29:30 to 31:39

Learn how AI is revolutionizing data interpretation and machine alignment in experiments.

“easier application than a kind of traditional computer because of these considerations of room temperature and also the power of just adding individual qubits.”

Streamlining Data Analysis with Machine Learning

31:40 to 33:38

Understand how machine learning aids in processing large volumes of experimental data.

“that we're after because the more stable the machine is, the better data we get.”

Data Production and Collaboration with AI

33:39 to 34:54

Discuss the relationship between data production in experiments and AI algorithm development.

“And we have a sort of first indication, is this experiment working?”
Hear the part that matters, and keep it.Open this episode in VO. Double tap your headphones to save a moment as you listen.
Get VO free

Transcript

Automatic transcript. May contain errors.

0:00Matthias Kling:Hey everyone, it's your host Russ Altman from the Future of Everything here. About a year ago, we released an episode on the future of ultra-fast electronics. It quickly became one of our most popular episodes, especially on YouTube. We're excited to re-share it with you today. Physicist Matthias Kling walks us through his study of photons and the things science and scientists can do with ultra-fast pulses of x-rays. The knowledge he's gaining could reshape fields like material science, artificial intelligence, ultra-fast and quantum computers, and medical diagnostics. We hope you'll tune in again wherever you get your podcasts.

0:37Matthias Kling:As a reminder, we post full video versions of each of our episodes on YouTube every Friday, so be sure to check those out if you haven't already. Before we get started, please remember to follow the show in whatever app you're listening to. That'll guarantee that you never miss the future of anything.

1:00Matthias Kling:So we all know that electronics are fast. Computers, fiber optics, they're operating on the scale of nanoseconds, which are one billionth of a second. There are a billion nanoseconds in a second. And that's how our computers and smartphones are working these days. But physicists have recently figured out ways to generate pulses of light that are at the scale of attoseconds, A-T-T-O seconds. There are a billion attoseconds in one nanosecond. And therefore, if you've ever heard of this word, it's a quintillion of them in a second. So what does this matter? Well, it means that we might be able to make measurements of physical systems that capture motions that are super fast and even make movies of chemical bonds forming or breaking or individual electrons emerging from the surface of a new material.

1:57Matthias Kling:It'll also lead to faster computers, including both traditional computers and quantum computers. Well, Matthias Kling, as a professor of photon science and applied physics at Stanford University and an expert at these very fast pulses of light and their applications. He's going to tell us how all of this technology might revolutionize the future of everything. Matthias, one of your areas of research expertise is ultra-fast electronics, ultra-fast photonics. What are the technologies or capabilities that are making these advances possible now? Yeah, thank you, Russ. First of all, thank you for having me.

2:37I love talking ultra fast. So when you think about the fastest processes that we're experienced to, that's, for instance, our computers, right? They run at gigahertz frequencies. So that's if you convert that to a time scale, it's nanosecond scales. So that's sort of scales that we're sort of used to, but then there's quite a few orders of magnitude of scales below that where things evolve even faster than electrons zipping around in your electronics. And that is, for instance, molecules rotating. That takes picosecond time, is on picosecond timescales, that's 10 to the minus 12 seconds.

3:19Matthias Kling:So a picosecond is a thousand times faster than a nanosecond. I'm just checking. That's right. That's a thousand times faster than a nanosecond. And then another yet a thousand times faster than a picosecond is a femtosecond. That's when molecular bonds break and recombine. And the timescales that I'm concerned with are even faster, autosecond timescales. So that's 10 to the minus 18 seconds. And it's a billions of a billions of a seconds. and you have to write a lot of zeros on the board to get to that one in the end. Yes. This is ATO just for people who are not familiar. A-T-T-O second is what we're talking about here.

4:02Matthias Kling:And if I'm keeping track of the numbers, there's a million of them in a nanosecond, which is already what our computers are operating at speed. Okay. Yeah. So, okay. And essentially one at a second compares to one second. That's about the heartbeat rate, right? As one second to the age of the universe. So just the ability of researchers to be able to look at these incredibly fast timescales is already mind blowing. And in fact, 20 years ago was, in 2001, in fact, was the magical year where two groups managed to measure the first out of second pulses, the groups of Ferenc Krauss and Pierre Agostini, based on a technique that Annelie developed that is called high harmonic generation, where you generate X-rays from intense laser interactions.

4:58and they have been awarded the Nobel Prize last year for this discovery. And there's a good reason that they were awarded the prize because these very short light flashes, they allow us to take movies. So imagine you're dancing around, right, in a disco or so, and you have that flashlight illuminating your dance, then you will see yourself in sort of still frame images dancing around the floor. And if you put them all together, you can sort of assemble the whole dance out of it.

5:26Matthias Kling:Yes. And in a similar way, if you use these extremely short light flashes that are just addoseconds short, we can actually flash them at electrons as they undergo motion in molecules or in nanosystems or whatever it is. And we can see how they move from A to B and so on. So they're so incredibly fast that we need these very, very short light flashes to sort of take a still frame image of their motion. So that's, thank you very much. So that's fantastically exciting. Atto seconds are our new favorite time unit. What kinds of things are happening in physics that you could take a movie of at atto seconds?

6:03Matthias Kling:So you mentioned that the heartbeat is every one second or so. So we can get nice pictures of a heartbeat. Since it takes about a second, I only have to divide that up, say, into a 10 or 100, 100th of a second or one-tenth of a second, and I can get a nice, smooth movie of one or two heartbeats. That's right. We don't need atto seconds to look at hearts. Oh, maybe I'm wrong. You'll tell me. At least the gross heartbeat, it would just be a very, very, very slow-mo. And many people know about this now because we all have slow-mo on our phones. If you take too many time points, it just takes forever for the heart to beat.

6:36Matthias Kling:So there must be interesting things happening at the Atto second timescale. Paint a picture for what kind of things we'll be able to see in these movies. Yeah, so imagine the first movie, in fact, that was taken was a movie that Edward Mubridge took at Stanford. Stanford University didn't exist at the time, but Stanford was this big horse farm. Yes. and the question was there if horses have all four hooves in the air as they are galloping right and they couldn't answer that question because it was just the motion was too fast and so they were debating whether this is the case or not and then Edward Mubridge had this really nice idea of assembling a set of cameras along the horse track and sort of triggering them one by one as the horse was going along the track.

7:29And so he recorded these still frame images of the horse along the track. And well, he did capture, in fact, one image where the horse has all four hooves in the air. So they were able to answer a very fundamental question that existed at the time with the first type of movie that was ever recorded. And we're taking this basic concept to the extreme. So we're looking at the fastest motions that you can imagine nowadays, and that is electrons zipping around atoms, zipping around small molecules. And so one very fundamental process we looked at, and there's actually a paper that just came out yesterday, is photoemission.

8:12The photoelectric effect is something that Einstein essentially was able to describe using quantum mechanics. and he was awarded the Nobel Prize for this discovery, for this description. And it's essentially you shine light on some metal, for instance, and what is emitted then as an action of the light impinging on this structure is an electron. Okay. And this electron has a certain kinetic energy and we can all measure that. And so you would think this was about 100 years ago, right? So at least his prize was. So you would think that this is well understood, right? 100 years of physics, I mean, we must understand this.

8:53But in fact, what was not understood until very recently, until Adam's second science came along, is how long it actually takes between the light being absorbed by the structure and the electron to come out. Yes. And this was assumed to be essentially instantaneous, right? So it was so fast that people said, okay, it's instantaneous. But that's not really true. So if you look at the details, you can find out that this electron actually, it really travels and it needs a little bit of time to come out. And the interesting thing is that the time it takes to come out has a lot to do with what kind of environment it's in, right?

9:32If it's in a molecule and an atom and how many other electrons are around and how it kicks around other electrons and things like that. And so this is quantum mechanics at its extreme. we're looking at the timescale it takes an electron to come out, for instance, of an atom as we shine light on it. And this is something we can measure. And it takes just about, let's say, 100 autoseconds to this latest measurement went all the way up to 700 autoseconds. And it tells us about the system. And so we can compare this to very detailed, very hard calculations. In fact, the data that we just published existed since about now.

10:08It was 2018. So that's six years ago, right? Six years ago, we took six years for the theory to catch up. So I like these experiments where we get the data and then theory is scratching their heads and they're like, oh, how?

10:23Matthias Kling:I need to explain this. I need to have a theory that makes. Yes. Okay. So that sounds fantastic. Let me just pause and make sure we understand. So are you taking movies of multiple electrons leaving the surface and then averaging them to get a sense? Or are you able to actually observe like individual movies of individual atoms leaving the surface? Yeah. So this is interesting. And this actually brings me to free electron lasers. Because there have been experiments that these Nobel laureates did since the invention or the birth of Atasek and Science using just normal tabletop setups in their labs.

11:05And in that case, they looked at what's called valence electrons. So these are electrons that are sort of the least bound electrons in some sense, and they're the easiest to remove. Yes. And they're typically, they're not very localized, right? So if you imagine you have a solid or you have a molecule, these electrons, they don't sit just on one particular atom, but they have some kind of delocalization over the whole structure. So your question is a very good one because using these tabletop techniques, it was very hard to answer where exactly that electron came from.

11:38Matthias Kling:Yes. Now we can use x-rays at much higher energies. And these x-rays penetrate very deep into atoms, very specific atoms. We can tune the energy just to, for instance, a nitrogen atom, an oxygen atom, things like that. And then look at the photo emission from that particular atom in a very large molecule. So we're very specific. Wow. Yeah, very sort of medical insertion in some sense. So it's very specific in what we can probe. and it's really great because the more specific we can look at where something is happening the more detailed the movie of course is that we will record in the end and the more detailed the comparison can be to the theory that we're trying to push ahead right because in an ideal world theory can explain everything so since we're talking about the future of everything you would hope that one day we have an ai and it uses uh theoretical calculations that are so advanced that we don't need to do experiments anymore, right?

12:36Right.

12:36Matthias Kling:I think this is actually my goal for biology as well. Yes. So my personal opinion on this is we'll never get there. But that's also because we're asking more and more complicated questions, right? So simple questions, probably theory, like if I drop an apple, right, and I let it fall to the ground, I can pretty much well predict when it arrives on the floor. But for complicated enough questions, theory has to make assumptions. And especially true for quantum mechanics. If I look at very complex systems, I need to make a lot of assumptions to describe what an experiment gives me. And so this interplay between experiment and theory to kind of push each other to develop the theory and make it better and to help us use then this sort of basic understanding to make much better materials, for instance, for solar cells, much better catalysts for producing new fuels and things like that.

13:31So there's real world impact in this very fundamental understanding that we are reaching.

13:37Matthias Kling:Yes, I've had guests on the Future of Everything podcast who are doing material science or electronic batteries. And it's always impressive to me that there is still a very empirical aspect to these fields. This is not a criticism, but the experimental work is very important because they don't always have the supporting theories. So what I'm understanding from you is that these measurements will give you the basis for the theories where they may be able to have a little bit more of an idea before they go into the laboratory of what to look for or what to build to get the properties that they're seeking.

14:14So this is definitely true for autosecond science. The field has evolved quite a bit in the last 20 years. So in the early days, people were happy to just understand what an autosecond pulse looks like and to be able to produce this very short light. But nowadays, the community is very brave. So we're looking into microelectronics. We're looking into medical applications. Ferenc Krauss, as an example, is looking into using some of the techniques developed in the field for what he calls molecular fingerprinting. So essentially, you shine the light through a drop of blood. You record the sort of light wave that comes out.

14:51and then from the exact sort of wave nature that you record from that, you can infer what type of diseases you have, right?

15:02Matthias Kling:Yes, because there are specific molecules that have a kind of, as you said, you use the word signature. These molecules have a signature that is unique and you can detect. Okay, let me ask a few questions about that because now you're getting close to things that I maybe understand fully. One of the things about living systems and blood is it's at room temperature or it's at body temperature, and the molecules are moving around a lot. And you were talking about focusing on – earlier you were saying you could focus on an individual nitrogen. That implies to me that you're going to have to do something to keep these molecules from moving too much, but maybe not.

15:38Matthias Kling:So is the issue of temperature – like do you have to freeze everything to make these measurements, or is room temperature or body temperature within range? So this is an excellent question because you're already motivated why it's interesting to study systems at the temperature where they're functioning in the body, right? So ideally, we don't want to have to freeze structures out to study their behavior because it will be very particular to that crystalline structure we created. And it might not reflect what we see in the real world, right? This interview would be very different if both of us were frozen.

16:14For sure. Exactly. So that's a good example. And in fact, this is where these X-ray free electron lasers come in. They can produce, and I have to back up a little bit to explain what that is. A free electron laser is essentially it starts with a linear accelerator that accelerates electrons, these tiny quantum particles to very high energies for the experts in the audience. At the moment, with our new superconducting accelerator, we can go up to four giga electron volts and in the future will be eight giga electron volts. So these are very, very high energies. And then we send these very high energy electrons through what's called an undulator.

16:56It's essentially a periodic magnetic structure that forces the electrons to wiggle, to oscillate a bit. And electrons don't like to oscillate. They essentially emit radiation as they do so. It's called synchrotron radiation. And using a few tricks, we can actually, in these three electron lasers, we can amplify that radiation. We can make it coherent instead of incoherent, how it's called. So it becomes laser-like. And so it really gains a lot in intensity. And these FELs, they have an incredible increase in brightness, in intensity over synchrotrons that have been generating X-rays for decades.

17:39And X-fels are around just since about, well, SELS has been around since 2009. So that's when the first experiments have been conducted, when it saw first light. our new superconducting accelerator was actually commissioned and taken into operation just last year and so it's a relatively young field and now we can use these extremely bright x-ray pulses imagine you know this is cannot at all compare to what you have at your doctor's office it's first of all it's laser-like which helps a lot with detecting for instance information that helps you to build 3D images instead of just projecting. So when you take an image of your tooth, let's say, at the dentist, sometimes it's really hard to see the details, right?

18:23When you use laser-like radiation, you take the same image. Even I, and I'm not a dentist, right, I can tell what I see because it's so short, and you get that sort of depth information, so you get 3D images. And this is sort of the type of radiation we generate, just a lot brighter. And it's also very short. So the pulses we get from the XFEL are, well, we can tune them. We can nowadays generate out-of-second light pulses. It's actually something that was invented here at LCLS by Argo Marinelli and James Krein, some two really fantastic scientists at the lab. And with these extremely short light pulses, we can, for instance, illuminate a biomolecule, Yeah.

19:07And then take an image, a diffraction image, X-ray diffraction image in one single shot. So one single X-ray illumination gives us the structure. And now imagine in real life, in nature, you don't just have that one structure. You would need to freeze it out to have that just that. You have many different ways how the protein could look like depending on temperature, depending on the environment and so on. And so what we do at these XFELs is we take all of these images. So we let the system at room temperature. We maybe stimulate some dynamics like we mix something in and then this thing folds or it catalyzes something.

19:46So enzymes are things that we look at. And we are following all of that at the same time. So essentially, we're taking these multitudes of images of all the things that are going on at the same time. And then we can use that information to really tell what nature is doing. And one of the really exciting things that nature is doing is to actually generate the oxygen that we breathe. So one of the most investigated and one of the success stories of these XFELs is the study of Photosystem II. It's essentially a system that sits in plants and is using just water and sunlight to generate oxygen. Somewhat key for life on Earth.

20:28Exactly. And everyone knows that. But the exact way how this is happening, this catalytic cycle is very complicated, has a couple of steps that people knew about, but they didn't quite know how the structure looks like and how it functions in reality. it was really possible at these exfels to study that for the first time. So now we have a sort of very fundamental understanding of how Photosystem II is generating oxygen from breaking up water with just sunlight. And this is, of course, amazing because if you, in fact, if you want to generate water as oxygen from water using our human technology, right, not what nature is doing, we would need to use very high electric currents or temperatures or whatever it is.

21:22It would be very, very inefficient as a process.

21:26Matthias Kling:So we're learning nature's secrets by looking at and taking movies as it happens. That's right. This is the future of everything with Russ Altman. more with Matthias Kling next.

21:47Matthias Kling:Welcome back to The Future of Everything. I'm Russ Altman, and I'm speaking with Professor Matthias Kling from Stanford University. In the last segment, Matthias explained to us some of the new capabilities in generating very rapid bursts of light that can control electrons and can make measurements on physical materials that allow us to see them move and change in real time. They're making movies that are super, super slow-mo. In this segment, Matthias will tell us a little bit about how all of this will lead to faster computers and electronics. He'll also tell us what the role of AI and machine learning is in all of these endeavors.

22:28Matthias Kling:So, Matthias, I wanted to ask you about an area that actually you're an expert in, which is ultra-fast electronics. Very early in the conversation, you referred to the fact that our current electronics are operating at a nanoscale timescale, but we've now been talking about out of seconds for quite a while. Is there a possibility of using that speed for our next generation of electronics? Yes. Thank you for this excellent question. So, I'm very passionate myself about advancing the speed of electronics. And one of the ways we dream of doing this is to use the waves themselves, the light waves themselves, and that electric field that the light wave has to steer electrons in circuitry.

23:13So at the moment, this is done just by applying a voltage and then you shift electrons around in these wires. And typically, there is some resistance that limits the speed. There is also other sort of limits. And so, in fact, the transistors nowadays, they have reached gigahertz level frequencies. So they're still operating at that nanosecond scale. But we dream about pushing that all the way to the autosecond scale, which in frequency space would not be gigahertz. It would be not even terahertz, but the next level, which is petahertz. And so you might wonder how far could we ever go? Well, there is a limit.

24:00Moore's law is going to have a limit. And the limit is essentially the speed of light. So you can't be faster than the speed of light. This is a very fundamental law. And essentially, as soon as we start moving electrons around at almost the speed of light, that's how fast we can go. But we're very, very far away from that. So we're about a million times below that speed. And so we want to use these light waves that actually the autosecond community has done a lot to produce light waves that are very well controlled, that are controlled to a minute detail on their actual sort of wave nature on the sub-cycle evolution.

Read the full transcript

24:39Imagine a light wave that's multiple cycles that you could draw on the board how the light wave propagates. And now I really look at the very fine details of how that light wave interacts with electrons. And so this principle has been, in fact, demonstrated in 2013 in the first prototype device where they were shining light and essentially it's a very simple device that has just a dielectric and then two metal contacts. And with that intense light, it was possible to turn the dielectric into a metal. So it was possible to essentially make it conducting. So it was a change of 10 to the 18 in conductivity.

25:20So this is pretty much what a transistor is doing. Right.

25:22Matthias Kling:So you go from electrons cannot pass to things can pass, but now it's light. And now it's really fast because you use the light wave itself to switch it on and off. So the switching speed we already demonstrated in the community, we can go to these petahertz timescales. So that's great. to make a real transistor is still a challenge because we need to integrate essentially this very concept into something that has now a million or a trillion transistors, right? And then make that all work in parallel. And of course, think about the light source that we would need. And there's many challenges, but it's a field that is, in my view, it's exponentially growing.

25:59There's more and more people jumping on it. And it's also really nice because this is something that with every sort of vision that you have and where you want to go, even if we might never actually reach that point where we make that sort of light speed electronic device, there's a lot of great discoveries that will happen along the way. And that is sort of the fun of it, right? That as a researcher...

26:24Matthias Kling:Yeah, I mean, you said there's a million, there's an opportunity to be a million times faster, but people might be very happy to be a thousand times faster for a little while while we're working out the details. Now, when you think about, I know this is very early and I know this is far off, but that doesn't stop me from asking these questions. When you think about these potential computers, are they going to be very energy efficient or are they likely to be, at least initially, very energy consumptive? Because as you know, in the world these days, people are now thinking not just about compute, but like compute per power requirement, because the power requirement is starting to scale to things that affect, you know, the temperature of the globe.

27:03Matthias Kling:So I know it's early and I know, but at least theoretically, are these things going to be low or are they going to be high energy consumers? I mean, we all aim to, of course, produce low energy consumer electronics. there's sort of one way that I can imagine we're getting there and it's essentially to enable quantum computing at room temperature. So at the moment quantum computers in fact you don't need a billion transistors. You just you actually double the computing power with every single qubit you're adding and so it's a very limited number that we need to have a huge sort of computing power in these in these quantum computers.

27:44And quantum computers are based on having coherence, having sort of, let's imagine you have a wave type thing that goes into a quantum computer and we need to preserve that wave nature. So we make these calculations with these waves and then we need to preserve that nature. And that is essentially what light wave electronics, that's how we call it, or petahertz electronics, is also doing. We're using that very coherent nature of light and we're preserving the sort of quantum nature of the process. and since we're investigating all of this at room temperature we're hoping one day we will have the right recipe to maybe not build a billion transistors on a circuitry but to have enough of these nodes to essentially build a quantum computer that could run at room temperature and that be very energy efficient very energy because we don't need to cool it down imagine to cool something down to cryogenic temperatures that's a huge plant that you need in in fact we have such plant here at LCLS.

28:41So we're running the superconducting accelerator at 2 Kelvin. So we need to cool it down to 2 Kelvin to reach that superconductivity where there's no resistance. So essentially, we can crank up the fields and we can generate these massive fields without generating a lot of heat. And so this becomes efficient in terms of the electron acceleration process, but it's very inefficient in terms of having to produce that cold helium in the first place. And so the same is true for quantum computers. At the moment, they're all using essentially cryogenic temperatures. And this is something that I'm dreaming of together with the community we could sort of move away from, develop either superconductors at room temperature that would enable us to do so, or advanced light wave electronics I just talked about, to do so.

29:28Matthias Kling:Very exciting. And so it's very interesting to hear that quantum computing might be the first easier application than a kind of traditional computer because of these considerations of room temperature and also the power of just adding individual qubits. Well, in the last couple of minutes, I wanted to ask you about the role of AI and machine learning because I know it's important in the field and it's popping up in everybody's life. And my understanding is it's even popping up in your work. It absolutely is. In fact, we have an MLAI program here at Slack, and it's a growing program that has essentially impact on all of our individual science programs.

30:10We're also very strongly connected, of course, with the Stanford community on this and what's happening in Silicon Valley. And so this is extremely exciting times, I have to say. There's many applications. The simple ones are, for instance, looking at the logbooks that we create. So when we take experimental data, the people that do the experiments, they enter information in logbooks. And this is typically very cryptic. And it's difficult, let's say, five years later to understand what someone had in mind when they wrote the logbook. Right. And use these large language models nowadays to really help you in interpreting essentially what you find in these logbooks.

30:47And so you can ask instead of scratching your head and wondering what the heck they meant at the time, you can ask AI what their interpretation is. And I think this can really help us. And that's a very simple application. We have even better ones in some sense. So imagine we have this really, really complicated machine that we use to produce the x-rays that starts with an injector. And then we have a two-mile accelerator. And there's many, many different units that need to play together. and in the past, we used to have expert operators. The facility runs 24-7 and the very best ones, they could align this machine within a brief amount of time, but you needed a lot of training to do so.

31:29Nowadays, we can use AI to essentially help us with this alignment. So it's a lot faster. We do save money doing so and we make the machine more efficient and we make it more suitable for all of these applications that we're after because the more stable the machine is, the better data we get.

31:47Matthias Kling:I find these two examples really surprising because you haven't talked about all the data that I'm sure you're collecting. I mean, of course, at the end of the day, you've talked about actually ways up front to get the data collection process to be more efficient. And on the back end, hey, you can say to yourself, well, five years ago, we did something and we didn't think it was that important. But all of a sudden now it becomes something very important. And can I use AI to help understand that? So I guess my final question is, what about AI to understand what you're observing? You were talking about three-dimensional movies and things like this.

32:19Matthias Kling:Is the AI going to play a role there? And of course, we're hoping it's true and not hallucinated. Almost certainly, right? So at the moment, I can tell you we produce with the new superconducting accelerator that produces up to a million pulses per second. So imagine you record images up to a million images per second. And each of these images has, I don't know what it is, let's say megabytes, right? So, sorry, megapixels. So it's a huge amount of data. In fact, it's so much data that we will struggle to store it somewhere. But the most important is people come here with their favorite biomolecule and all they want is the structure, right?

33:00So, and if we produce this huge amount of data, it becomes very difficult for researchers to essentially go through the data, interpret what they see, and also sort it into this is good data, this maybe is not so interesting, and just storing what's really interesting and analyzing it. And this we can absolutely streamline with ML. So this is being used as we speak. We have collaborations with exascale computing centers to essentially look at this data in real time and to send the data off to a superconductor and then, sorry, supercomputer, and then essentially analyze the structure within just minutes.

33:41And so we can put a structure in, we record this huge amount of data, and the ML algorithm essentially predicts then the structure from that data that was recorded, something that a human being could never do because it's just too much data to go through. And we have a sort of first indication, is this experiment working? Should we spend more time on a particular substance or not? Or should we maybe go to the next one, right? And so these very important questions, we don't want to base any photons we're sending to the experiments. So the faster we can analyze the data and the more comprehensive the information is that we can get out of the data, the better.

34:16And this is just one example, but essentially ML is so important these days in running these very complex machines and in analyzing the huge amount of data that I cannot think of a world where I would separate the two again. So I do think also that facilities like the ones we operate here, LCLS at Slack, they can help the ML AI community because we are data producers, right? So we produce a huge amount of data and they can test essentially the models and the different algorithms they are developing if they're applicable to these types of problems. And I think there is a lot to be learned, not just from us on our side, from the ML community, but I think also on the other end, right?

35:02So the other way around. So we're essentially the data providers and they try different algorithms on this data.

35:10Matthias Kling:Well, that's great. And I think that's where we'll leave it. Thank you for this introduction to Adosec in physics, the way it will help build better computers and also use and help AI in the analysis of the data across a wide range of applications. Thanks to Matthias Kling. That was the future of ultra-fast electronics. Thanks for tuning into this episode. You know, we have more than 250 episodes in the back catalog. So you have access to a wide range of discussions on a diversity of topics that will give you a picture of the future of everything. Meanwhile, if you're enjoying the show, please consider telling your friends, family, and colleagues about it because that's the best way to grow our audience and get feedback about how we're doing.

35:52Matthias Kling:You can connect with me on many social media platforms at rbaultman or at russbaultman on LinkedIn, Threads, Blue Sky, and Mastodon. You can also follow Stanford Engineering at Stanford ENG or at Stanford School of Engineering. If you'd like to ask a question about this episode or a previous episode, please email us a written question or a voice memo question. We might feature it in a future episode. You can send it to thefutureofeverything at stanford.edu. All one word, the future of everything. No spaces, no underscores, no dashes. The future of everything at stanford.edu. Thanks again for tuning in.

36:37Matthias Kling:We hope you're enjoying the podcast.

From the publisher

About a year ago we released an episode on the future of ultrafast electronics and it quickly became one of our most popular episodes on YouTube. We’re excited to re-share it with you today. Physicist Matthias Kling walks us through his study of photons and the things science can do with ultrafast pulses of x-rays. The knowledge he’s gaining could reshape fields like materials science, artificial intelligence, ultrafast and quantum computers, and medical diagnostics. We hope you’ll tune in again wherever you get your podcasts. And as a reminder, we post full video versions of each of our episodes on YouTube every Friday, so be sure to check those out if you haven’t already.

Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu.

Episode Reference Links:

Connect With Us:

Chapters:

(00:00:00) Introduction

Russ Altman introduces guest Matthias Kling, a professor of photon science and applied physics at Stanford University.

(00:02:21) Ultrafast Electronics

New technologies enabling ultrafast photonics and electronics.

(00:05:40) Attosecond Science Applications

Capturing electron and molecular movements with attosecond pulses.

(00:10:23) Real-Time Molecular Measurements

How free-electron lasers enable detailed, atom-specific measurements.

(00:15:02) Free-Electron Lasers

Using light waves to capture images of molecules at room temperature.

(00:22:28) Electronics at Light Speed

Whether attosecond science could enable computing at petahertz speed.

(00:26:24) Lightwaves & Quantum Computing

How lightwave electronics could allow for energy-efficient quantum computers.

(00:29:29) AI Meets Ultrafast Science

AI's role in optimizing research and data collection in ultrafast electronics.

(00:31:47) The Future of Ultafast Electronics

How attosecond science, computing, and AI converge to accelerate innovation.

(00:35:25) Conclusion

Connect With Us:

Episode Transcripts >>> The Future of Everything Website

Connect with Russ >>> Threads / Bluesky / Mastodon

Connect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook


Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

More from The Future of Everything

All 67 episodes
Best of: The future of ultrafast electronicsThe Future of Everything · 36 min
Listen in VO