John Preskill on Quantum Computing | Transcription
Transcription for the video titled "John Preskill on Quantum Computing".
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And what was the revelation that made scientists and physicists think that a quantum computer could exist? It's not obvious. You know, a lot of people thought it couldn't. OK. The idea that a quantum computer would be powerful was emphasized over 30 years ago by Richard Feynman, the Caltech physicist. And it was interesting how he came to that realization. Feynman was interested in computation his whole life. Yeah. You know, he had been involved during the war in Los Alamos. He was the head of the computation group. He was the guy who fixed the little mechanical calculators and he had a whole crew of people who were calculating and he figured out how to flow, you know, the work from one computer to another, all that kind of stuff. And as computing technology started to evolve, you know, he followed that. And in the 1970s, a particle physicist like Feynman, that's my background, too, got really interested in using computers to study the properties of elementary particles, like the quarks inside a nucleus. You know, we know a proton isn't really a fundamental object. It's got little beans rattling around inside, but they're quantum. And a gal man who's good at names called them quarks. Yeah. And now we have we've had a theory since the 1970s of how quarks behave. And so in principle, you know, everything about the theory, you can compute everything, but you can't because it's just too hard. And people started to simulate that physics with digital computers in the 70s. And there were some things that they could successfully compute and some things they they couldn't because it was just too hard. The resources required, you know, the memory, the time. We're out of reach. And so Feynman in the early 80s said, you know, nature is quantum mechanical damage. So if you want a simulation of nature, it should be quantum mechanical. You should use a quantum system to behave like another quantum system at the time. We called it a universal quantum simulator. Okay. And now we call it a quantum computer. And the idea caught on about 10 years later when Peter Schor made the suggestion that we could solve problems which don't seem to have anything to do with physics, which are really things about numbers like finding the prime factors of a big integer. And that caused a lot of excitement in part because the implications for cryptography are a bit disturbing. But then physicists, good physicists started to consider, can we really build this thing? Yeah. And some concluded and argued fairly cogently that no, you couldn't. Because of this difficulty that it's so hard to isolate systems from the environment well enough for them to behave quantumly. And so it took a few years for that to sort out sort of at the theoretical level.
Insights Into Quantum Computing
Can we really build a quantum computer? (03:06)
In the mid 90s, we developed a theory called quantum error correction. It's about how to encode the quantum state that you'd like to protect in such a clever way that even if there are some interactions with the environment that you can't control, it still stays robust. But at first, that was just kind of a theorist fantasy. It was a little too far ahead of the technology. But 20 years later, the technology is catching up. So now this idea of quantum error correction has become something you can do in the lab. Yeah. And how does quantum error correction work? I've seen a bunch of diagrams. So maybe this is difficult to explain, but how would you explain it? Well, I would explain it this way. I don't think I've said the word entanglement yet. No. Well, I have been checking off all the bingo words yet. Okay. So let's talk about entanglement because it's part of the answer to your question, which I'm still not done answering. What is quantum physics? So what do we invite entanglement? It's really the characteristic way, maybe the most important way that we know in which quantum is different from ordinary stuff from classical. And what does it mean entanglement? It means that you can have a physical system, which has many parts, which have interacted with one another. So it's in kind of a complex correlated state of all those parts. And when you look at the parts one at a time, it doesn't tell you anything about the state of the whole thing, the whole thing is in some definite state. There's information stored in it. You know, you'd like to access that information. Let me be a little more concrete. Suppose it's a book. Okay. It's a book. It's a hundred pages long. So if it's an ordinary book, a hundred people could each take a page and read it. They know what's on that page and then they get together and talk and now they know everything that's in the book, right? But if it's a quantum book written in qubits where these pages are very highly entangled, there's still a lot of information in the book, but you can't read it the way I just described. You can look at the pages one at a time, but a single page, when you look at it, it just gives you random gibberish. It doesn't reveal anything about the content of the book. Why is that? It's there's information in the book, but it's not stored in the individual pages. It's encoded almost entirely in how those pages are correlated with one another. That's what we mean by quantum entanglement information stored in those correlations, which you can't see when you look at the parts one at a time. So you asked about quantum error correction. Yeah.
What is quantum error correction? (05:50)
What's the basic idea? It's to take advantage of that property of entanglement because let's say you have a system of many particles and the environment is kind of kicking them around. It's interacting with them because you can't really completely turn off those interactions. No matter how hard you try. But suppose we've encoded the information in entanglement. So say if you look at one atom, it's not telling you anything about the information you're trying to protect. Yeah. So the environment isn't learning anything when it looks at the atoms one at a time. And this is kind of the key thing that what makes quantum information so fragile is that when you look at it, you disturb it. This ordinary water bottle isn't like that. You know, let's say we knew it was either here or here and we didn't know. I would look at it. What I find out is here. I was ignorant of where it was to start with. Now I know. But with a quantum system, when you look at it, you really change the state. There's no way to avoid that. So if the environment is looking at it in the sense that information is leaking out to the environment, that's going to mess it up. So we have to encode the information so the environment so to speak can't find out anything about what the information is. And that's the idea of quantum error correction. If we encoded an entanglement, the environment is looking at the parts one at a time, but it doesn't find out what the protected information is. Yeah, no.
Working with Probabilities (07:20)
So in other words, it's kind of measuring probability the whole way along, right? I'm not sure what you mean by that. So is it Grover's algorithm that was like as it as quantum bits roll through go through gates, the probability is determined of what what information is being passed through. What's being computed? Yeah. So Grover's algorithm is a way of sort of doing an exhaustive search through many possibilities. Okay. You know, like, let's say I'm trying to solve some problem like, you know, a famous one is the traveling salesman problem. You know, I told you what the distances are between all the pairs of cities. And now I want to find the shortest route I can that visits them all. That's a really hard problem. And it's still hard for a quantum computer, but not quite as hard because there's a way of solving it, which is to try all the different routes and measure how long they are and then find the one that's shortest and you've solved the problem. The reason it's still hard to solve is there's such a vast number of possible routes. But what Grover's algorithm does is it speeds up that exhaustive search. And in practice, it's not that big a deal. What it means is that, you know, if you have the same processing speed, you can handle about, you know, twice as many cities before the problem becomes too hard to solve as you could if you were using a classical processor. But as far as what's quantum about Grover, it takes advantage of the property in quantum physics that probabilities. I might tell me if I'm getting too inside baseball. No, no, this is perfect. That probabilities are the squares of amplitudes. This is interference. Yeah. Again, this is another part of the answer. We can spend the whole hour answering the question, what is quantum physics? Another essential part of it is what we call interference. And this is really crucial for understanding how quantum computing works. And that is that probabilities add, you know, if you know the probability of one alternative and you know the probability of another, then you can add those together and find the probability that one or the other occurred. And it's not like that in quantum physics. The famous example is the double slit interference experiment. You know, I'm sending electrons, let's say, it could be basketballs, but it's an easier experiment to do with electrons at a screen and there are two holes in the screen. And you can try to detect the electron on the other side of the screen. And when you do that experiment many times, you can plot a graph showing where the electron was detected, you know, in each run or make a histogram of all the different outcomes. And the graph wiggles. Okay. So if it were, if you could say there's some probability of going through the first hole and some probability of going through the second and each time you detected it, it went through either one or the other, there'd be no wiggles in that graph. Mm-hmm.
As the interference that makes it wiggle. And the essence of the interference is that nobody can tell you whether it went through the first slit or the second slit. The question is sort of inadmissible. And this interference then occurs when we can add up these different alternatives in a way which is different from what we're used to. It's not right to say that the electron was detected at this point because it had some probability of going through the first hole and some probability of going through the second and we add those probabilities up. That doesn't give the right answer. The different alternatives can interfere. Mm-hmm. And this is really important for quantum computing because what we're trying to do is enhance the probability or the time it takes to find the solution to a problem. And this interference can work to our advantage. We want to have, you know, when we're doing our search, we want to have a higher chance of getting the right answer and a lower chance of getting the wrong answer. And if the different wrong answers can interfere, they can cancel one another out and it enhances the probability of getting the right answer. So sorry, it's such a long winded answer. But this is how Grover's algorithm works. Okay. It can speed up exhaustive search by taking advantage of that interference phenomena. Well, this is kind of one of the underlying questions among many of the questions from Twitter. You've hit our record for most questions asked. But basically, many people are wondering what quantum computers really will do if and when it becomes a reality that they outperform classical computers.
What Quantum Computers will be Good at (12:52)
What are they going to be really good at? Well, you know what? I'm not really sure. And I think, you know, if you look at the history of technology, it would be hubris to expect me to know. It's a whole different way of dealing with information. This quantum information is not just, you know, a quantum computer is not just a faster way of computing. It deals with information in a completely new way because of this interference phenomenon, because of entanglement that we've talked about. And I think we have limited vision when it comes to predicting decades out what the impact will be of an entirely new way of doing things, information processing in particular. I mean, you know this well. If we go back to the 1960s and people are starting to put a few transistors on a chip, where is that going to lead? Nobody knew. Even early days of the internet, even the first. Yeah, good example. Even the first browser. No one really knew what anyone was going to do with it. So it makes total sense. For good or ill. And, but we have some ideas. You know, I think why are we confident there will be some transformative effect on society.
New Materials (14:11)
Of the things we know about, and I emphasize again, probably the most important ones are things we haven't thought of. When it comes to applications of quantum computing. The ones which will affect everyday life, I think are better methods for understanding and inventing new materials, new chemical compounds, things like that can be really important. You know, if you find a better way of capturing carbon by designing a better catalyst, or you can design pharmaceuticals that have new effects, materials that have unusual properties, these are quantum physics problems because those properties of the molecule or the material really have to do with the underlying quantum behavior of the particles. And we don't have a good way for solving such problems or predicting that behavior using ordinary digital computers. That's what a quantum computer is good at. It's good, but maybe not the only thing it's good at. But one thing it should certainly be good at is telling us quantitatively how quantum systems behave. And in the two contexts I just mentioned, there's a little question that there will be practical impact of that. So it's not just doing the traveling salesman problem through the table of elements for like why it can find these compounds. It's much more than that. If it were, that wouldn't be very efficient. Exactly. Yeah. No, it's much trickier than that. And like I said, the exhaustive search though conceptually, it's really interesting that quantum can speed it up because of interference.
Exponential Speedup (16:20)
From a practical point of view, it may not be that big a deal. It means that, well, like I said, in the same amount of time, you can solve an instance, which is twice as big of the problem. So what we really get excited about are the so-called exponential speedups. And that was why Schor's algorithm was exciting in 1994 because factoring large numbers was a problem that had been studied by smart people for a long time. And on that basis, the fact that there weren't any fast ways of solving it was pretty good evidence. It's a hard problem. Actually, we don't know how to prove that from first principles. Maybe somebody will come away, you know, come along one day and figure out how to solve factoring very fast on a digital computer. It doesn't seem very likely because people have been trying for so long to solve problems like that. And it's just intractable with ordinary computers. You could say the same thing about these quantum physics problems. Maybe some brilliant graduate student is going to drop a paper on the archive tomorrow, which we'll say here I solve quantum chemistry and I can do it on a digital computer. But we don't think that's very likely because we've been working pretty hard on these problems for decades and they seem to be really hard. And so those cases like these number theoretic problems which have cryptological implications and tasks for simulating the behavior of quantum systems were pretty sure those are hard problems. Classically, and we're pretty sure quantum computers, I mean we have algorithms that have been proposed, but which we can't really run currently because our quantum computers aren't big enough on the scale that's needed to solve problems really people really care about.
Prob To Simulate Chem (18:10)
Yeah, so maybe we should jump to one of the questions from Twitter which is related to that. So Travis Scholten asked what are the most problem pressings in physics, let's say specifically around quantum computers that you think substantial progress ought to be made in to move the field forward? I know Travis, he was an undergranny. Oh, okay. How are you doing Travis? So the problems that we need to solve to make quantum computing closer to realization at the level that would solve problems people care about. Well, let's go over where we are now. Yeah, definitely. Okay. So people have been working on quantum hardware for 20 years working hard and there are a number of different approaches to building the hardware and nobody really knows which is going to be the best. We have where I think we're far from collapsing to one approach which everybody agrees has the best long-term prospects for scalability. And so it's important that a lot of different types of hardware are being pursued. And we can come back to what some of the different approaches are later. So where are we now? We think in a couple of years we'll have devices with about 50 qubits to 100 and we'll be able to control them pretty well. And that's an interesting range because even though it's only 50 to 100 qubits, it doesn't sound like that big a deal. But that's already too many to simulate with a digital computer even with the most powerful super computers today. So from that point of view, these are relatively small near-term quantum computers which we'll be fooling around with over the next five years or so are doing something that's kind of super classical. At least we don't know how to do exactly the same things with ordinary computers. Now that doesn't mean they'll be able to do anything that's practically important, but we're going to try. Okay, we're going to try. And there are ideas about things we'll try out including sort of baby versions of these problems in chemistry and materials and ways of speeding up optimization problems. Nobody knows how well those things are going to work at these small scales. Part of the reason is not just that the number of qubits is small, but they're also not perfect. So we can perform elementary operations on pairs of qubits, which we call quantum gates like the gates in ordinary logic. But they have an error rate, a little bit below, you know, an error every 100 gates. So that if you have a circuit with 1000 qubits, there's a lot of noise. So exactly like it does, for instance, 100 qubit quantum computer really mean 100 qubit quantum, 100 qubit quantum computer. Or do you need a certain amount of backup going on? Well, I think in the near term, we're going to be trying out and probably we have the best hopes of hybrid classical methods with some kind of classical feedback. You try to do something on the quantum computer. You make a measurement that gives you some information.
Then you change the way you did it a little bit and try to converge on some better answer. That's one possible way of addressing optimization that might be faster on a quantum computer. But I just wanted to emphasize that the number of qubits isn't the only metric and how good they are. And in particular, the reliability of the gates, how well we can perform them. That's equally important. So anyway, coming back to Travis's question, well, there are lots of things that we'd like to be able to do better. But just having much better qubits would be huge. Right, so if you more or less with the technology we have now, you can have a gate error rate of a few parts in a thousand. If you can improve that by orders of magnitude, then obviously you could run bigger circuits. That would be very enabling. Even if you stick with a hundred qubits, just by having a circuit with more depth, more layers of gates that increases the range of what you could do. And that's always going to be important.
A single error (23:24)
That's looking how crappy that is. A gate error rate, even if it's one part in a thousand. It's pretty lousy compared to if you look at where classical processors are. A billion transistors in it. You don't worry about the... It's gotten to the point where there is some error protection built-in at the hardware level in a processor because we're doing these crazy things like going down to an 11 nanometer scale for features on a chip. So how are folks trying to deal with interference right now? What types of devices? That's interesting too because there are a range of different ways to do it. I mentioned that we could store information, we can make a qubit out of a single atom, for example. That's one approach. You have to control a whole bunch of atoms and get them to interact with one another. One way of doing that is with what we call trapped ions. That means the atoms have electrical charges. That's a good thing because then you can control them with electric fields. You can hold them in a trap and you can isolate them, like I said, in a very high vacuum. So they're not interacting too much with other things in the laboratory, including stray electric and magnetic fields. But that's not enough because you've got to get them to talk to one another.
You've got to get them to interact. We have this set of desiderato, which are kind of in tension with one another. On the one hand we want to isolate the qubits very well. On the other hand we want to control them from the outside and get them to do what we want them to do. Eventually we want to read them out. You have to be able to read out the result of the computation. But the key thing is the control. You have two of those qubits in your device to interact with one another in a specified way and to do that very accurately. You have to have some kind of bus that gets the two to talk to one another. The way they do that in an ion trap is pretty interesting. It's by using lasers and controlling how the ions vibrate in the trap. You can have a laser kind of excite wiggles of the ion. Then by determining whether the ions are wiggling or not, you can go address another ion. That way you can do a two-cubit interaction and you can do that pretty well. Another way is really completely different. What I just described was encoding information at the one atom level. Another way is to use superconductivity circuits in which electric current flows with out any dissipation. In that case you have a lot of freedom to engineer the circuits to behave in a quantum way. There are many nuances there. The key thing is that you can encode information now in a system that might involve the collective motion of billions of electrons. Yet you can control it as though it were a single atom. Here's one oversimplified way of thinking about it. Suppose you have a little loop of wire and there's current flowing in the loop. It's a superconducting wire so it just keeps flowing. Normally there would be resistance which would dissipate that as heat. But not for the superconducting circuit which of course has to be kept very cold to stay superconducting. You can imagine in this little loop that the current is either circulating clockwise or counterclockwise. That's a way of encoding information. It could also be both at once. That's what makes it a qubit. In that case even though it involves lots of particles, the magic is that you can control that system extremely well. I mentioned individual electrons. That's another approach. Put the qubit in the spin of a single electron. You also mentioned better qubits. What did you mean by that? What I really care about is how well I can do the gates. There's a whole other approach which is motivated by the desire to have much, much better control over the quantum information than we do in those systems that I mentioned so far. Like superconducting circuits and trapped ions. That's actually what Microsoft is pushing very hard. We call it topological quantum computing. It's topological. The word physicists and mathematicians love. It means, "Well, we'll come back to what it means." Just tell you what they're trying to do. They're trying to make a much, much better qubit which they can control much, much better using a completely different hardware approach. It's very ambitious because at this point it's not even clear they have a single qubit. But if that approach is successful and it's making progress, so I think we will see a validated qubit of this type soon, maybe next year. Then nobody really knows where it goes from there. Suppose it's the case that you could do a two qubit gate with an error rate of 1 in a million instead of 1 in a thousand. That would be huge. Now scaling all these technologies up really challenging from a number of perspectives including just the control engineering. So how are they doing it? Or attempting to do it? You could ask, "Where did all this progress come from over 20 years or so?" For example, with the superconducting circuits, a sort of crucial measure is what we call the coherence time of the qubit, which roughly speaking means how much it interacts with the outside world, the longer the coherence time, the better. So the rate of what we call decoherence is essentially how much it's getting buffeted around by outside influences. And for the superconducting circuits, those coherence times have increased about a factor of 10 every three years going back 15 years or so. Wow. Now it won't necessarily go on like that indefinitely, but in order to achieve that type of progress, better materials, better fabrication, better control, the way you control these things is with microwave circuitry. Not that different from the kind of things that are going on and communication devices. And all those things are important, but I think going forward, the control is really the critical thing.
Coherence Times (30:58)
Coherence times are already getting pretty long. I mean, having them longer is certainly good, but the key thing is to get two qubits to interact just the way you want them to. And even if there is no, I keep saying the key thing is the environment. It's not the only key thing. Yeah. Because you have some qubit, like if you think about that, electrons spin one way of saying it is, I said it can be both up and down at the same time. Well, there's a simpler way of saying that. It might not point either up or down. It might point some other way. But there are really a continuum ways of ways that could point. That's not like a bit. See, it's much easier to stabilize a bit because it's got just two states. Yeah. But if it can kind of wander around in the space of possible configurations for a qubit, that makes it much harder to control. Hmm. And, you know, people have gotten better at that, a lot better at that in the last few years. Interesting. So Joshua Herman asked what engineering strategy for quantum computers do you think has the most promise? Yeah. So I mentioned some of these different approaches. And I guess I'll interpret the question as which one is the winning horse? Yeah. I know better than to answer that question. They're all interesting.
Applications And Implications Of Quantum Computing
Engineering Quantum Computers (32:19)
Okay. But I, for the near term, the most advanced are superconducting circuits and trapped ions, which is why I mentioned those first. And I think that will remain true, you know, over the next five to ten years. There are other technologies that have the potential like these topologically protected qubits. To surpass those, but it's not going to happen real soon. I kind of like superconducting circuits because there's so much face space of things you can do with them, you know, of ways you can engineer and configure them and imagine scaling them up. They have the advantage of being faster than the time it takes, the cycle time, the time to do a gate is faster than with the trapped ions. The basic physics of the interactions is different. In the long term, those electron spins could catapult ahead of these other things. That's something that you can naturally do in silicon. And, you know, it's potentially easy to integrate with silicon technology. Right now the qubits and gates aren't as good as the other technologies, but that can change. And I mean, from a theorist perspective, this topological approach is very appealing. And so we can imagine, you know, it takes off maybe ten years from now and it becomes the leader. So I think it's important to emphasize we don't really know what's going to scale the best. Right. And are there multiple attempts being made around programming quantum computers? Yeah. I mean, some of these companies that are working on quantum technology now, which includes well-known big players like IBM and Google and Microsoft and Intel, but also a lot of startups now, they are trying to encompass the full stack. So they're interested in the hardware and the fabrication and the control technology. But also the software, the applications, the user interface, all those things are certainly going to be important eventually. Yeah. They're pushing it almost to like an AWS layer, where you interact with your quantum computer in a server farm and you don't even touch it. Yeah. That's how it will be in the near term, I think.
Quantum Smart Cards (35:05)
You're not going to have most of us want to have a quantum computer, you know, sitting on your desktop or in your pocket, maybe someday. In the near term, it'll be in the cloud and you'll be able to run applications on it by some kind of web interface. And, you know, ideally that should be designed so the user doesn't have to know anything about quantum physics in order to program or use it. And I think that's part of what some of these companies are moving toward. Do you think it will get to the level where it's in your pocket? How do you deal with that when you're below one Kelvin? Well, if it's in your pocket, it probably won't be one Kelvin or no. Yeah, probably not. So what do you do? Well, there's one approach as an example, which I guess I mentioned in passing before. Or maybe it doesn't have to be at such low temperature and that's nuclear spins. Because they're very weakly interacting with the outside world, you can have quantum information in a nuclear spin, which I'm not saying that it would be undisturbed for years. But seconds, which is pretty good. And you can imagine that getting significantly longer. Someday you might have a little quantum smart card in your pocket. The nice thing about that particular technology is you can do it at room temperature. So I'm long coherence times. And if you go to the ATM and you're worried that there's a rogue bank that's going to steal your information, one solution to that problem, I'm not saying there aren't other solutions, is to have a quantum card where the bank will be able to authenticate it without being able to forge it. We should talk about the security element. Kevin Sue asked what risk would quantum computers pose to current encryption schemes, so public key, and what changes should people be thinking about if quantum computers come in the next five years, ten years.
Public Key Crypto (37:25)
Yeah, quantum computers threaten systems that are in widespread use. Whenever you're using a web browser and you see that little padlock, you're at an HTTPS site, you're using a public key crypto system to protect your privacy.
Cryptography and Privacy (37:46)
And those crypto systems rely for their security on the presumed hardness of computational problems. That is, it's possible to crack them, but it's just too hard. So RSA, which is one of the ones that's widely used, as typically practiced today, to break it, you'd have to do something like factor a number, which is a number of problems. A number, which is over 2000 bits long, to 2048. And that's too hard to do now, but that's what quantum computers will be good at. Another one that's widely used is called elliptic curve. Cryptography doesn't really matter exactly what it is, but the point is that it's also vulnerable to quantum attack. Yeah. So we're going to have to protect our privacy in different ways when quantum computers are prevalent. What are the attempts being made right now? Well, I mean there are two main classes of attempts. Okay. One is, just to come up with a cryptographic protocol, not so different conceptually from what's done now, but based on a problem that's hard for quantum computers. How are you doing? And it turns out that what has sort of become the standard way doesn't have that feature. And there are alternatives that people are working on. We speak of post-quantum cryptography, meaning the protocols that we'll have to use when we're worried that our adversaries have quantum computers. And I don't think there's any proposed cryptosystem, although there's a long list of them by now, which people think are candidates for being quantum resistant, for being unbreakable or hard to break by quantum computers. I don't think there's any one that, you know, the world has sufficient confidence in now that it's really hard for a quantum adversary, that we're all going to switch over. But it's certainly time to be thinking about it. You know, when people worry about the privacy, of course, different users have different standards. But the U.S. government sometimes says they would like a system to stay secure for 50 years. They like to be able to use it for 20, roughly speaking, and then have the intercepted traffic be protected for another 30 after that. So I don't think, though I could be wrong, that we're likely to have quantum computers that can break those public-key cryptosystems in 10 years. But in 50 years, it seems not unlikely. And so we should really be worrying about it. And the other one is actually using quantum communication for privacy. Oh, yes. So in other words, if you and I could send qubits to one another instead of bits, it opens up new possibilities. So the way to think about these public-key schemes, or one way that we're using now, is I want you to send me a private message. And I can send you a lockbox. It has a padlock on it. But I keep the key. But you can close up the box and send it to me. But I'm the only one with the key. So the key thing is that if you have a padlock, you can reverse engineer the key. Of course, it's a digital box and key. But that's the idea of public-key. The idea of what we call quantum key distribution, which is a particular type of quantum cryptography, is that I can actually send you the key, or you can send me your key. But why can't any eavesdropper then listen in and know the key? Well, it's because it's quantum. And remember, it has that property that if you look at it, you disturb it. So if you collect information about my key, or if the adversary does, that will cause some change in the key. And there are ways in which we can check whether what you received is really what I sent. And if it turns out it's not, or it has too many errors in it, then we'll be suspicious that there was an adversary who tampered with it. And then we won't use that key, because we haven't used it yet. We're just trying to establish the key. So we do the test to see whether an adversary interfered, if it passes the test, then we can use the key. And if it fails the test, we throw that key away, and we try again. That's how quantum cryptography works. But it requires a much different infrastructure than what we're using now. We have to be able to send qubits. Well, it's not completely different, because you can do it with photons. And of course, that's how we communicate through optical fiber now, we're sending photons. And it's a little trickier sending quantum information through an optical fiber, because of that issue that interactions with the environment can disturb it. But nowadays, you can send quantum information through an optical fiber over tens of kilometers with low enough error rates. So it's useful for communication.
Quantum Repeaters and Error Correcting (43:35)
Wow. Of course, we'd like to be able to scale that up to global distances. Sure. And there are big challenges in that. But anyway, so that's another approach to the future of privacy that people are interested in. And does that necessitate quantum computers in both ends? Yes, but not huge ones. Okay. And the reason, well, yes and no. At the scale of tens of kilometers, no. And you can do that now. There are prototype systems that are in existence. But if you really want to scale it up, then in other words, to send things longer distance. Then you have to bring this quantum error correction idea into the game. Okay. Because at least with our current photonics technology, there's no way I can send a single photon from here to China without there being a very high probability that gets lost in the fiber somewhere. So we have to have what we call quantum repeaters, which can boost the signal. But it's not like the usual type of repeater that we have in communication networks now. The usual type is you measure the signal and then you resend it. That won't work for quantum because as soon as you measure it, you're going to mess it up. So you have to find a way of boosting it without knowing what it is. And of course, it's important that it works that way because otherwise the adversary could just intercept it and resend it. And so it will require some quantum processing to get that quantum error correction and the quantum repeater to work. Yeah. But it's a much more modest scale quantum processor than we would need to solve hard problems. Okay. Gotcha. And what are the other things that you're both excited about and worried about for potential business opportunities? Snehan Keckery asks, "Butting entrepreneurs, what should they be thinking about in the context of quantum computing?" Yeah. I mean, there's more to quantum technology than computing. And something which has good potential to have an impact in the relatively near future is improved so that it's not a good thing. And that is improved sensing. Quantum systems, partly because of that property that I keep emphasizing that they can't be perfectly isolated from the outside.
A new frontier in physics: entanglement (46:21)
They're good at sensing things. And sometimes you want to detect it when something in the outside world messes around with your qubit. And again, using this technology of nuclear spins, which I mentioned, you can do it at room temperature potentially, you can make a pretty good sensor and it can potentially achieve higher sensitivity and spatial resolution. You can look at things on shorter distance scales than other existing sensing technologies. So one of the things people are excited about are the biological and medical implications of that. If you can monitor the behavior of molecular machines, you know, probe biological systems at the molecular level using very powerful sensors, that would surely have a lot of applications. So one interesting question you can ask is can you use these quantum air correction ideas to make those sensors even more powerful? That's another area of current basic research for where you could see significant potential economic impact. Interesting. So in terms of your research right now, what are you working on that you find both interesting and incredibly difficult? Everything I work on is both interesting and incredibly difficult. Okay. Well, let me change direction a little from what we've been talking about so far. Well, let me tell you a little bit about me. Sure. So I didn't start out interested in information in my career. You know, I'm a physicist and I was trained as an elementary particle theorist studying the fundamental interactions and the elementary particles. And that drew me into an interest in gravitation because one thing that we still have a very poor understanding of is how gravity fits together with the other fundamental interactions. The way physicists usually say it is we don't have a quantum theory of gravity, at least not one that we think is complete and satisfactory. So I'm interested in that question for many decades. Then I kind of got sidetracked because I got excited about quantum computing. But you know, I've always looked at quantum information, not just as a technology. You know, I'm a physicist. I'm not an engineer. I'm not trying to build a better computer necessarily, though I think that's very exciting and worth doing. And if my work can contribute to that, it's very pleasing. But I see quantum information as a new frontier in the exploration of the physical sciences. Sometimes I call it the entanglement frontier. You know, we like to talk about frontiers. Short distance frontier. That's what we're doing at CERN, you know, in the Large Hadron Collider, trying to discern new properties of matter at distances which are shorter than we've ever been able to explore before. And there's a long distance frontier in cosmology. You know, we're trying to look deeper into the universe and understand its structure and behavior at earlier times. Those are both very exciting frontiers. This entanglement frontier, I think, is increasingly going to be at the forefront of basic physics research in the 21st century. And by entanglement frontier, I just mean scaling up quantum systems to larger and larger complexity where it becomes harder and harder to simulate those systems, you know, with our existing digital tools. And so that means we can very well anticipate the types of behavior that we're going to see. I think that's a great opportunity for new discovery. And that's part of what's going to be exciting, even in the relatively near term. When we have 100 qubits, you know, there are some things that we can do to understand the behavior of the dynamics of, you know, a highly complex system of 100 qubits that we've never been able to experimentally probe before. And that's going to be very interesting. But what we're starting to see now is that these quantum information ideas are connecting to these fundamental questions about gravitation and how to think about it quantumly. And it turns out, as is true for most of the broader implications of quantum physics, the key thing is entanglement. And we can think of the microscopic structure of space time, the geometry of where we live. Geometry just means, you know, who's close to who else? And if we're in the auditorium and, you know, I'm in the first row and you're in the fourth row, you know, the geometry is how close we are to one another. So of course, that's very fundamental in both space and time. How far apart are we in space? How far apart are we in time? Is geometry really a fundamental thing? Or is it something that's kind of emergent from some even more fundamental concept? It seems increasingly likely that it's really an emergent property, that there's something deeper than geometry. What is it?
Concepts And Education In Quantum Mechanics
Space-time is an emergent geometry (52:45)
We think it's quantum entanglement. That you can think of the geometry as arising from quantum correlations among parts of a system. And that's really what defines who's close to who. And so we're trying to explore that idea more deeply. And one of the things that comes in is the idea of quantum error correction. Remember, the whole idea of quantum error correction was that we could make a quantum system behave the way we wanted to because it's well protected against the damaging effects of noise. And it seems like quantum error correction is part of the deep secret of how space-time geometry works. It has a kind of intrinsic robustness coming from these ideas of quantum error correction that makes space, you know, meaningful. So that it doesn't just evaporate when you tap on it. If you wanted to, you know, you could think of the space-time, the space that you're in and the space that I'm in as parts of a system that are entangled with one another. So what would happen if we broke that entanglement and, you know, your part of space became disentangled from my part? What we think that would mean is that there'd be no way to connect us anymore. There wouldn't be any path through space that starts over here with you and ends with you. It would become broken apart into two pieces. So it's really the entanglement which holds space together, which keeps it from falling apart into little pieces. And, you know, we're trying to get a deep grasp of what that means. And how do you make any progress on that? That seems like the most unbelievably difficult problem to work on. It's difficult because, well, for a number of reasons, but in particular, because it's hard to get guidance from experiment, which is how physics, historically, has advanced. And although it was fun a moment ago to talk about what would happen if we disentangled your part of space from mine, I don't know how to do that in the lab right now. So, of course, part of the reason is we have the audacity to think we can figure these things out just by thinking about them. Maybe that's not true. Nobody knows, right? We should try. Yeah. Solving these problems is a great challenge. And, you know, it may be that the apes that evolved on Earth are not, don't have the capacity to understand things like the quantum structure of space-time. But maybe we do. So we should try. Now, in the longer term, and maybe not such a long term, I think maybe we can get some guidance from experiment. And in particular, what we're going to be doing with quantum computers and, you know, the other quantum technologies that are becoming increasingly sophisticated in the next couple of decades, is we'll be able to control very well, highly entangled, complex quantum systems. And so that should mean that in a laboratory on a tabletop, I can sort of make my own little toy space-time with an emergent geometry arising from the properties of that entanglement. And I think that'll teach us lessons because systems like that are the type of system that, because they're so highly entangled, digital computers can't simulate them. It seems like only quantum computers are potentially up to the task. So that won't be quite the same as, you know, disentangling your side of the room for mine in real life. But we'd be able to do it in a laboratory setting, you know, using model systems, which I think would help us to understand the basic principles better. Wild. Yeah, desktop space-time seems pretty cool. I figured it out. Yeah, it's pretty fundamental.
Quantum Non-Locality (56:59)
We didn't really talk about what people sometimes, we didn't implicitly, but not in so many words. We didn't talk about what people sometimes call quantum non-locality. Okay. And it's another way of describing quantum entanglement, actually. There's this notion of Bell's theorem that when you look at the correlations among the parts of a quantum system that they're different from any possible classical correlations. And some things that you read give you the impression that you can use that to instantaneously send information over long distances. It is true that if we have two qubits, electrons, spins, and they're entangled with one another, then what's kind of remarkable is that I can measure the quantum system. I can measure my qubit, like to see along some axis, whether it's up or down, and you can measure yours, and we will get perfectly correlated results. When I see up, you'll see up, say, and when I see down, you'll see down. And sometimes people make it sound like that's remarkable. That's not remarkable in itself. Somebody could have flipped a pair of coins so they came up both heads and both tails and given one to you. It's flipped them apart to me. And gone a light year apart, and then we go, "Yeah, mine's heads, mine's heads." And then they call it quantum transportation on YouTube. Of course, what's really important about entanglement that makes it different from just those coins is that there's more than one way of looking at a qubit. We have what we call complementary ways of measuring it. You can ask whether it's up or down along this axis or along that axis. There's something like that for the coin. There's just one way to look at it. And what's cool about entanglement is that we'll get perfectly correlated results if we both measure in the same way. But there's more than one possible way that we can measure. And so what sometimes gets said or the impression people get is that that means that when I do something to my qubit, it instantaneously affects your qubit, even if we're on different sides of the galaxy. But that's not what entanglement does. It just means they're correlated in a certain way. And when you look at yours, if we have maximally entangled qubits, you just see a random bit. It could be a zero or one each occurring with probably only one half. And that's going to be true no matter what I did to my qubit. And so you can't tell what I did by just looking at it. It's only that if we compare notes later, we can see how they're correlated and that correlation holds for either one of these two complementary ways in which we could both measure. And it's that fact that we have these complementary ways to measure that makes it impossible for a classical system to reproduce those same correlations. So that's one misconception that's pretty widespread. Another one is this about quantum computing, which is in trying to explain why quantum computers are powerful, people will sometimes say that they're not going to be able to do it. And people will sometimes say, "Well, it's because you can super pose.
Why Cant Quantum (01:00:48)
I used that word before. You can add together many different possibilities." And that means that whereas an ordinary computer would just do a computation once, acting on a superposition, a quantum computer can do a vast number of computations all at once. There's a certain sense in which that's mathematically true if you interpret it right. But it's very misleading because in the end, you're going to have to make some measurement to read out the result. And when you read it out, there's a limited amount of information you can get. You're not going to be able to read out the results of some huge number of computations in a single shot for measurement. So really the key thing that makes it work is this idea of interference, which we discussed briefly when you asked about Grover's algorithm. The art of a quantum algorithm is to make sure that the wrong answers interfere and cancel one another out so the right answer is enhanced. And that's not automatic. It requires that the quantum algorithm be designed in just the right way. Right. So the diagrams I've seen online at least involve, usually you're squaring the output as it goes along and then essentially that flips the correct answer to the positive and the others are in a negative position. Is that accurate? I wouldn't have said it the way you did because you can't really measure it as you go along. And once you measure it, the magic of superposition is going to be lost. It means that now there's some definite outcome or state. So to take advantage of this interference phenomenon, you need to delay the measurement. Remember when we were talking about the double slit and I said, if you actually see these wiggles in the probability of detection, which is the signal of interference, that means that there's no way anybody could know whether the electron went through hole one or hole two.
Feynmans Superposition (01:02:54)
And it's the same way with quantum computing. If you think of the computation as being a superposition of different possible computations, it wouldn't work. There wouldn't be a speed up. If you could know which of those paths the computation followed, it's important that you don't know. And so you have to sum up all the different computations and that's how the interference phenomenon comes into play. To take a little sidetrack, you mentioned Feynman before and before we started recording, you mentioned working with him. I know I'm in the Feynman fan club for sure. What was that experience like? We never really collaborated. I mean, we didn't write a paper together or anything like that. But we overlapped for five years at Caltech. Arrived here in 1983. He died in 1988. We had offices on the same corridor and we talked pretty often because we were both interested in the fundamental interactions and in particular what we call quantum chromodynamics. It's our theory of how nuclear matter behaves, how quarks interact, what holds the proton together, those kinds of things. And one big question is, you know, what does hold the proton together? Why don't the quarks just fall apart? So that was an example of a problem that both he and I were very interested in and which we talk about some time. Now, you know, this was pretty late in his career. I actually, when I think about it now, when I arrived at Caltech, that was 1983. Feynman was born in 1918, so he was 65. I'm 64 now. So maybe he wasn't so old. But at the time, he seemed pretty ancient to me since I was 30. And those who interacted with Dick Feynman, you know, when he was really at his intellectual peak in the 40s and 50s and 60s, probably saw even more extraordinary intellectual feats than I witnessed interacting with the 65-year-old Feynman. But he just loved physics, you know, and he just thought everything was so much fun. And he loved talking about it. He wasn't as good a listener as a talker. But actually, well, that's a little unfair, isn't it? It was kind of funny because Feynman, he always wants to think things through for himself. Okay. Or to, you know, from first principles rather than rely on the guidance from experts who have thought about these things before. Well, that's fine. You know, you should try to understand things as deeply as you can on your own and sort of reconstruct the things you have. You know, you have to reconstruct the knowledge from the ground up. That's very enabling and, you know, it gives you new insights. But he was a little too dismissive, in my view, of, you know, what the other guys knew. But I could slip it in because, you know, I didn't tell him, Dick, you should read this paper by Polly Akoff. Well, maybe I did, but he wouldn't even have heard that because he saw that problem that you're talking about. Yeah. But I knew what Polly Akoff had said about it. So I would say, "Oh, well, you know, why don't we look at it this way?" And so he thought I was, you know, I was having all these insights, but the truth was the difference between Feynman and me in the mid-1980s is I was reading literature and he wasn't. And probably if he had been, it would have been well served. But that wasn't the way he liked to work on things. Yeah. And wanted to find his own approach. And of course, that had worked out pretty well for him throughout his career. What other qualities did you notice about him when he was, you know, roaming the corridors? What difference did he have? He'd always been drumming. Oh. So you would know he was around because he'd actually be walking down the hallway drumming on the wall. With hands or with sticks or? No hands. Okay. He used to be tapping. He's the bongo thing. Yeah. And so that was one thing. Okay. He loved to tell stories. You know, you probably read the books that Ralph Layton put together based on the stories Feynman told. And I mean, Ralph did an amazing job, I think, of capturing Feynman's personality in writing those stories down because I'd heard a lot of them. Okay. I'm sure he told the same stories to many people many times because he loved telling stories. But the book really captures his voice pretty well. You know, if you had heard him tell some of these stories and then you read the way Ralph Layton transcribed them, you can hear Feynman talking. And so at the time that I knew him, one of the experiences that he went through was, you know, he was on the Challenger Commission after the Space Shuttle blew up. And so he was in Washington a lot of the time, but he'd come back from time to time and he would sort of, you know, sit back and relax in our seminar room and start bringing us up today and all the weird things that were happening on the Challenger Commission. That was pretty fun. That's really cool. A lot of that got captured in the second volume, I guess it's the one called, "What do you care what other people think?" There's a chapter about him telling stories about the Challenger Commission. He was interested in everything. You know, it wasn't just physics. Yeah. And he was very interested in biology. He was interested in computation. I remember how excited he was when he got his first IBM PC. Probably not long after I got to Caltech.
Teaching engineers (01:09:50)
It was what they called the AT. We thought it was a pretty sexy machine. I had one too. And, you know, we couldn't wait to start programming it in basic. That was so much fun. There was a question that I was kind of curious to your answer. So, Tika asks about essentially teaching about quantum computers. So, they say, "Many kids in grade 10 can code. Some can play with machine learning tools without knowing the math. Can quantum computing become as simple and or accessible?" Maybe so. So, at some level, you know, when people say quantum mechanics is counterintuitive, it's hard for us to grasp. It's so foreign to our experience.
Education advice (01:10:47)
That's true. The way things behave at the microscopic scale, or what you discussed earlier, are really different from the way ordinary stuff behaves. But I think it's a question of familiarity. And what I wouldn't be surprised by is that if you go out a few decades, kids who are 10 years old are going to be playing quantum games. That's an application area that doesn't get discussed very much. But there could be a real market there because, you know, people love games. And quantum games are different. And the strategies are different. And what you have to do to win is different. And if you play the game enough, you start to get the hang of it. And so, I think, I don't see any reason why kids who have not necessarily deeply studied physics can't get a pretty good feel for how quantum mechanics works. You know, the way ordinary physics works, maybe it's not so intuitive. Newton's laws, you know, Aristotle couldn't get it right. We had to keep pushing on something to get it to keep moving. That wasn't right. And you had to, you know, Galileo was able to roll balls down a ramp and things like that. And you see he didn't have to keep pushing it, keep it moving. And you could see that it, you know, was uniformly accelerated in a gravitational feel. Newton took that to a much more general and powerful level. And, you know, you fool around with stuff and you get the hang of it. And I think quantum stuff can be like that. You know, we'll experience it in a different way. But when we have quantum computers in a way, you know, that opens the opportunity for trying things out and see what happens. And after you played the game enough, you start to anticipate, and actually it's an important point about the applications. One of the questions you asked near the beginning was, what are we able to do with quantum computers? And I said, I don't know. So how are we going to discover new applications? And might just, at least in part, be fooling around. You know, a lot of classical algorithms that people use on today's computers were discovered or that they were powerful, was discovered by experimenting and by trying it. I don't know, what's an example of that? Well, the simplex method, you know, that we use in linear programming. I don't think there was a mathematical proof that it was fast at first. But people did experiments and they said, hey, this is pretty fast. Well, you see it. You're seeing it a lot now in machine learning. Yeah. Well, that's a good example. You test it out a million times over when you're running simulations, and it turns out that's what works. Yeah. What about, so kind of like following the threat of education and maybe your political interest, given it's, yeah, the year that it is, do you have thoughts on how you would adjust or change STEM education? Well, no particularly original thoughts. Okay. But I do think that STEM education, we shouldn't think of it as we're going to need this technical workforce, and so we better train them. I think the key thing is we want the general population to be able to reason effectively, and to recognize when an argument is phony and when it's authentic, and to think about, well, how can I check whether what I just read on Facebook is really true? And I see that as part of the goal of STEM education. You know, when you're teaching kids in school how to understand the world by doing experiments, by looking at the evidence, by reasoning from the evidence, this is something that we apply in everyday life too. And so I would like to, I don't know exactly how to implement this, but I think we should have that perspective. Now we're trying to educate a public which is going to eventually make critical decisions about our democracy, and they should understand what's, how to tell when something is true or not. I mean, you know, that's a hard thing to do in general, but you know what I mean. Yeah. That there are some things that if you're a person with some, I mean, it doesn't necessarily have to be technical, but if you're used to evaluating evidence and making a judgment based on that evidence about whether it's a good argument or not, you can apply that to all the things you hear and read and make better judgments. What about on the policy side? Someone, let's see, JJ Francis asked that if you or any of your colleagues, whatever, consider running for office, what's the case about science policy in the US? Well, it would be good if we had more scientifically trained people in government. Very few members of Congress, I know one, Bill Foster's a physicist in Illinois, was a particle physicist, and he worked at Fermi Lab, and now he's in Congress, and I think very interested in, you know, the science and educational policy aspects of government. Rush Holt was a congressman from New Jersey who had a background in physics.
Personal Reflections And Opinions On Education And Entrepreneurship
What are the best uses of science and its study of science administration in government? (01:17:13)
He retired from the house a couple of years ago, but he was in Congress for something like 18 years, and I think he had a positive influence because he had a voice that people respected when it came to science policy, and having more people like that would help. Now another thing, it doesn't have to be elective office. Right. And there are a lot of technically trained people in government, many of them making their careers in agencies that deal with technical issues. Department of Defense, of course, there are a lot of technical issues. In the Obama administration, we had two successive secretaries of energy who were very, very good physicists. Steve Schu was a Nobel Prize-winning physicist, and then Ernie Moniz, so real authority. On nuclear energy and weapons, and that kind of expertise makes a difference in government. Now the Secretary of Energy is Rick Perry. It's a different background. Yeah, you could say that. Just kind of historical reference, what policies did they put in place that you think were, that you really felt their hand as a physicist. Yeah, move forward. You mean in particular? During the Obama administration. Yeah. Well, I think the Department of Energy, DOE, tried to facilitate technical innovation by, you know, seeding new technologies by supporting startup companies that were trying to do things that would improve battery technology and solar power and things like that, which could benefit future generations. And I think they had an impact by doing that. You don't have to be a Nobel Prize-winning physicist. I think that's a good idea. I think that the administration felt that was a priority, made a difference. And the point of physicists as Department of Energy was, if nothing else, highly symbolic of how important those things are. And on the quantum side, someone asked Vikas Karad, he asked where the quantum valley might be. Well, I don't know, but you look at what's happening in the last couple of years. There have been a number of quantum startups. And a notable number of them are in the Bay Area. Why so? Well, that's where the tech industry is concentrated and where the people who are interested in financing innovative technical startups are concentrated. So if you are an entrepreneur interested in starting a company and you're concerned about how to fundraise for it, it kind of makes sense to locate in that area. That's what's sort of happening now and may continue, of course, and why not be like that? And definitely nothing less forever. But I would say that's the place that the Silicon Valley is likely to be quantum valley, the way things are right now. Well then, what about the physicists who might be listening to this? If they're thinking about starting a company, do you have advice for them? Just speaking very generally, if you're putting a team together, you know, different people have different expertise. We'll take quantum computing as an example, you know, like we were saying earlier, some of the big players and the startups, they want to do everything, they want to build hardware, figure out better ways to fabricate it, better control, better software, better applications.
What kind of people make the best business entrepreneurs? (01:21:57)
Nobody can be an expert on all those things. So, you know, of course, you'll hire a software person to write your software and you know, microwave engineer to figure out your control. And of course, that's the right thing to do. But I think in that arena, and it probably applies to other entrepreneurial activity relating to physics, being able to communicate across those boundaries is very valuable. And you can see it in quantum computing now, that if a man or woman who's involved in the software has that background, but there's not a big communication barrier talking to the people who are doing the control engineering, that can be very helpful. And I think it makes sense to give some preference to the people who may be are comfortable doing so or have the background that stretches across more than one of those areas of expertise. I think that can be very enabling in a technology arena like quantum computing today, where we're trying to do really, really hard stuff and you know, you don't know whether you'll succeed and you want to give it your best go by seeing the connections, you know, between those different things. Would you advise someone then to maybe teach or try and explain it to, I don't know, their young cousins? Because I think like Feynman maybe recognizes the king of communicating physics, at least for a certain period of time. How would you advise someone to get better at it so they can be more effective? Practice. Well, you know, there are different aspects of that. This isn't what you meant at all, but I'll say it anyway, because what you asked brought it to mind. If you teach, you learn. And we had this odd model in the research university that a professor like me is supposed to do research and teach. Why don't I just, why don't we hire teachers and researchers? Why do you have the same people doing both? Well, part of the reason for me is most of what I know or what I've learned since my own school education ended is knowledge I acquired by trying to teach it. And, you know, to keep our intellect rejuvenated, I mean, we have to have that experience of trying to teach new things that we didn't know that well before to other people. That deepens your knowledge. Just thinking about how you convey it makes you ask questions that you might not think to ask otherwise, and you say, "Hey, I don't know the answer to that." And then you have to try to figure it out. So I think that applies at varying levels to any situation in which a scientist or somebody with a technical background is trying to communicate. By thinking about how to get it across to other people, we can get new insights. We can look at it in a different way. And so it's not a waste of time. Aside from the benefits of actually successfully communicating, we benefit from it in this other way. But other than that, have fun with it. You know, don't look at it as a burden or some kind of task you have to do along with all the other things you're doing. It should be a pleasure. And when it's successful, it's very gratifying. So if you put a lot of thought into how to communicate something and you think people are getting it, that's one of the ways that somebody in my line of work can get a lot of satisfaction. If now were to be your opportunity to teach a lot of people about physics and you could just point someone to things, who would you advise someone to be? They want to learn more about quantum computing. They want to learn about physics. What should they be reading? What YouTube channels should they follow? What should they pay attention to? Well, one communicator who I have great admiration for is Leonard Suskin, who is at Stanford.
Feynman and Susskind, quantum computing (01:27:06)
You mentioned Feynman as the great communicator, and that's fair. But in terms of style and personality of physicists who are currently active, I think Leonard Suskin is the most similar to Feynman, of anyone I can think of. He's a no bullshit kind of guy. He wants to give you the straight stuff. He doesn't want to water it down for you. But he's very gifted when it comes to making analogies and creating the illusion that you're understanding what he's saying. If you just go to YouTube and search Leonard Suskin, you'll see lectures that he's given at Stanford where they have some kind of extension school for people who are not Stanford students, people in the community. A lot of them in the tech community because it's Stanford. He's given courses and on quite sophisticated topics. But also on more basic topics. He's in the process of turning those into books. I'm not sure how many of those have appeared. But he has a series called, I think, The Theoretical Minimum, which is supposed to be the gentle introduction to different topics like classical physics and quantum physics and so on. So he's pretty special, I think, and his ability to do that. I need to subscribe. Actually, here's a question then. In the things you've relearned while teaching over the past, I guess it's 35 years now. Shit, is that right? Something like that?
Things you learned from teaching with Steve (01:29:02)
That's true. What were the big thing? What were the revelations? Well, that's how I learned quantum computing for one thing. I was not at all knowledgeable about information science. It wasn't my training. Back when I was in school, physicists didn't learn much about things like information theory, computer science, complexity theory. One of the great things about quantum computing is its interdisciplinary character. It brings these different things into contact, which traditionally had not been part of the common curriculum of any community of scholars. I decided 20 years ago that I should teach a quantum information class at Caltech. I worked very hard on it that year. Not that I'm an expert or anything, but I learned a lot about information theory and things like channel capacity and computational complexity. I learned a lot about how we classify the hardness of problems and algorithms, things like that, which I didn't really know.
I really felt I was kind of close enough to the cutting edge on most of those topics (01:30:28)
Very well, I had sort of a passing familiarity with some of those things from reading some of the quantum computing literature. But that's no substitute for teaching a class, because then you have to synthesize it and figure out your way of presenting it. Most of the notes are typed up, and you can still get to them on my website. But that was pretty transformative for me, because although it didn't, and it was easier than 20 years ago, I guess than it is now, because it was such a new topic. But I really felt I was kind of close enough to the cutting edge on most of those topics. By the time I'd finished the class, that I wasn't intimidated by another paper I'd read or a new thing I'd hear about those things. So that was probably the one case where it really made a difference in my foundation of knowledge, which enabled me to do things. I had the same experience in particle physics. When I was a student, I read a lot, and I was very broadly interested in physics.
When I was a student, I read a lot... (01:31:49)
But the first time I was still at Harvard at the time, but I taught a similar course here. In my late 20s, I'm just a year or two out of graduate school, and I decided to teach a very comprehensive class on elementary particles, in particular quantum chromodynamics, the theory of nuclear forces like we talked about before. And it just really expanded my knowledge to have that experience of teaching that class. I still draw on that. I can still remember that experience, and I think I get ideas that I might not otherwise have because I went through that.
Plan To Continue Education
I want to go back to school... (01:32:36)
I want to get involved now. I want to go back to school, or maybe teach a class. I don't know. I'm stopping you. All right. Thanks, John. Okay. Thank you, Craig.