Quantum Computing with guest Mark Jackson

Quantum computing expert, Mark Jackson Ph.D, joins us to discuss the latest state of quantum computing and how he see it progressing over the next five plus years. We discuss the technology, opportunities, and cybersecurity aspects.

## Webinar Transcript

**Marv: **[00:00:03] Ok, thanks for joining us again today. We have a special guest I think you’ll really enjoy, Dr. Mark Jackson, who works for Cambridge Quantum Computing. He and I met each other a few years ago and he’s agreed to come on and tell us a little bit about where the state of quantum computing sits and what is happening in this crazy world of quantum computing that is just starting to open up. So let me start out by letting Mark explain to us a little bit about what he’s been doing over the past few years as he got into this business. I know your degree is in quantum computing, so maybe you could give us a little bit of background on that as well, Mark.

**Mark: **[00:00:39] Sure thing. So thank you, Marvin. Thank you, Jim, for having me on your show. So, yes, I’m the quantum evangelist at Cambridge Quantum Computing, and it’s really an incredible time. It’s historic. I think there’s no other word for it. This will be a time that they they talk about hundreds of years from now, because this is really the first time that we’ve made an advancements in how we we store and we process and we calculate. When I give presentations about quantum computing, I often point out that computers, as we know it, were invented about two hundred years ago. And even though they look simplistic, it’s actually still the basic ideas of our computers today, like the laptop that sits in front of me. Quantum computing is the first advance in that. And so that’s why it’s so exciting. We can talk about that a little bit later. But since you asked about my my background, so, yes, my my degree is in quantum physics. So I got my Ph.D. in string theory, Columbia University in New York. And and for many years I was in academia doing research and string theory and cosmology. And so I did things like like the Big Bang and black holes and grand unified theories and such and. I left out briefly to do my own startup, and then I briefly returned to academia, and this was about five years ago, I started hearing about quantum computing and of course, this didn’t exist commercially when I was a student.

**Mark: **[00:02:07] And so it was it was really interesting to me to hear that there were now these computers being built. And I wanted to kind of get into this. But because I didn’t have a background specifically in that and I didn’t really know of anyone, I sort of felt sidelined. I felt like I was kind of a cheerleader. I would I would sometimes give presentations about it to to public audiences. But I felt like I was missing out because I knew this would be an important field. And I was very lucky about four and a half years ago, to have dinner with a friend of mine in Berkeley is a math professor there, and he offered to introduce me to Ileus Khan, who was the founder and CEO of Cambridge Quantum. And it was very well timed because they were about three years old and they were based in the U.K., of course, and they were looking to expand us. And so I was I was the first hire in the US. And so so it’s been about four years that I’ve been there. And it’s it’s been quite a ride.

**Marv: **[00:03:02] And it’s excellent. How about just giving us a little bit of foundation on what is quantum computing? I’m sure we’ll have people in the audience that don’t track it as much as Jim and I have. And even then, you can’t be an expert on it like you are in any sense.

**Mark: **[00:03:17] Sure. Sure. So first, let’s let’s review what normal computers are and then I can explain how quantum computers are different. So normal computers recall use bits of binary digits. Right. So bits are just ones and zeros. Everything on a computer at the most basic level are ones and zeros which are like little On-Off switches. Right. So it’s on or it’s off. There’s nothing in between. A quantum computer can can be in between a zero and a one, and that that sounds a little strange, but I often think of it like like queen of Bits is sort of like a queen sitting on a table, its heads or tails, its one or the other, but a quantum bit or Cubitt, as we call it, is like a queen spinning in space. So you kind of arbitrarily engelhardt’s any way that you want. It could happen to be. Heads like purely heads or purely tails, but there’s a lot in between and and funny enough that the reason that’s a really good analogy is that it’s actually mathematically identical to how these cubits work. If you actually think of of a vector or an arrow pointing in space like a queen would, depending on which face it’s facing. That’s a mathematically identical cube. It works. And so quantum computers use these cubits to do a calculation. And you might ask, well, why is that so important? Why do we go to all this trouble of having quantum bits and things like that? The reason that that’s so important is because a quantum bit can do two things at once.

**Mark: **[00:04:51] It’s considering the zero keys and the one simultaneously. And the technical thing for that is superposition. So we say that this cubit is in a superposition of the zero state, the one state, and it’s weighted differently in general. Right. So it’s it might be like 10 percent for the zero case and 90 percent for the one case. And then there’s even a more subtle feature called the Quantum Faits. But the point is that these qubits can represent several different configurations at once. One cubit represents two configurations at once, two cubits represents four configurations at once, three cubits represents eight configurations at once and so forth. So every time you add a cubit, you’re doubling the number of configurations that you consider. So 30 cubits corresponds to about a billion different combinations. And again, that’s all handled simultaneously and not every problem can take advantage of. So it’s actually only very specific problems that that can do calculations in this way. But if it can do calculations in this way, it can be much, much faster than a normal computer. In fact, it’s so much faster that we think quantum computers could solve problems that never could have we never could have hoped to have solved with normal computers. So it’s not just a faster computer. It’s so much more powerful for some problems that that we should be able to solve problems that we never could have hoped to solve before.

**Marv: **[00:06:15] So, Mark, I know the Cambridge quantum computing is not really being in the business of building quantum computers. There are several companies around the world doing that. Explain to us a little bit about what they really do and why it’s so important.

**Mark: **[00:06:29] Sure. So Cambridge Quantum focuses on the software of quantum computer. So so just like with normal computers, there’s hardware and their software. And we have have focused on every area of quantum software package. So we have developed our own compiler, which is hardware agnostic. So that means that you can use your favorite quantum programming language to write a program and execute it on your favorite quantum hardware. So the compiler does the translation and it finds the most efficient way to run the program on your favorite quantum computer. We have machine learning and we have chemistry and we have natural language processing as well as cybersecurity. And so so we’ve been developing software in all of these areas. There are many groups building hardware for quantum computers, so there’s there’s big corporations like Honeywell and IBM and Google, there are startups like Iron Cube and Righetti that are many academic groups. So there’s lots of groups doing this. There are a lot of technical approaches to building a quantum computer. So there’s not just one way to build a quantum computer. There’s many different ways. Some of the more popular ways include superconducting quantum computers. And you might see these pictures.

**Mark: **[00:07:43] They sort of look like chandeliered with wires hanging down and they get lowered into a cryogenic chamber. So this this refrigerator that’s kept it near absolute zero. There is the Iron Chop method in which you you manipulate these charged particles, ions using lasers. There’s photonic where you’re actually using individual photons, usually on a chip to do the calculations. So there’s lots of different ways to do this. And they each have their own advantages and disadvantages. And I truly don’t know who who the winner will be. There will probably be a few winners. We’ve seen extraordinary progress. It’s been much faster than Moore’s Law to the rate of progress in quantum hardware. Just in the four years since I’ve been in the business, we’ve seen incredible progress. And both, both IBM and Google predicts that they will have a thousand cubits within two or three years and they think they will have a million cubits within ten years. And so that really will be incredible. We we think that we’re about three to five years away from being able to commercially deploy quantum computers. So so that’s that’s how close we are.

**Jim: **[00:08:52] Mark, I know in the software side, like the new hot thing is kind of go the open source route where you you build an open source community. Is that kind of what’s happening in the software side of it?

**Mark: **[00:09:04] Is that that’s actually a very well timed question, because there is a nonprofit group called the Unitary Fund, which which awards many grants and in open source projects. And the compiler that we’ve developed, we call the tickets. We are moving towards not necessarily open source, but right now it’s open license so everyone can use it free of charge. There’s there’s no issues with licensing or permissions or anything. The only thing that we ask is that you give us feedback on how we can make it better. And if you write an article using tickets that you just credit us in the article. But as time goes on, as time goes by, we we might be moving in the direction of open sourcing. Yes.

**Marv: **[00:09:51] I heard the reporters fly over the reflecting jets. So, Mark, I know I know that part of the problem with quantum computers is they have a large noise component and it’s hard to control the noise and continue the entanglement between whatever the sources are, photons, et cetera. So where do you think these quantum computers are relative to useful, mature maturity? I mean, when are we really going to do something like a chemistry or physics problem like you’re looking at?

**Mark: **[00:10:22] Sure, sure. So. So just to clarify for the audience, when we talk about noise and quantum computers, we don’t literally mean that they make a lot of noise. So so when we say noise, we mean that they have a large error rate and that means the qubits don’t do what we want them to do. So so this could be the qubits change their answer for no apparent reason. Sometimes they interfere with other qubits in a way that we don’t want them to. Sometimes we do a measurement and we don’t actually measure the answer that we thought we were going to get. So, so, so qubits have a lot of sources of noise and we pretty much rise all these different types and take this into account. And and yes, you’re exactly right. Right now, quantum computers are still too noisy and that means that it’s still too likely that they’ll make mistakes. Many people probably aren’t aware that normal computers, when they have bits, they they do have noise. There is a chance that a bit will make a mistake. It’s not often, but it’s very easy to Kretz with normal computers. And the way that we correct it is we just make triplicate copies of bits. So instead of using one bit source information, we use three bits to do that. And so in the unlikely case that one bit flips and makes a mistake, the other two will indicates, well, the answer is supposed to be. That’s right. If we just agreed to take the majority, then it’s very obvious which bit was wrong. And so assuming that it’s it’s very unlikely that we’ll make a mistake, we can get away with just triplicates.

**Mark: **[00:11:55] So you might ask, well, why can’t we just do that with quantum computers and play the same game of error correction it’s called. And the problem is that quantum physics has some rules which are a bit funny. And the rules are that you can’t copy quantum information. You can move it around, but you can’t copy it. Exactly. And the other rule is that once you measure something, you’ve destroyed some of the information. And so just the act of measuring it itself disturbs it. And so it at first blush, it might seem impossible to even do er connection with quantum computers because we can’t make copies of it and we can’t even measure it to see what might have gone wrong. And so you could be forgiven for thinking that it would be impossible to do error correction. Fortunately, a very smart person, professor at MIT named Peter Shaw, he developed a way to do quantum error correction. And so now we have all sorts of sophisticated ways of doing this. What you do is you actually spread the information on one qubits among several cubits. And it’s it’s a complicated mathematical way of doing this. But a bit by spreading out this information, there’s actually clever ways that you can measure the other qubits to identify a source of error and then correct it all while leaving the original cubit alone. So it’s it’s kind of funny that there’s this loophole. You can measure the other qubits, the auxiliary qubits believe that your cubitt alone.

**Mark: **[00:13:22] And in this way you can cleverly correct for errors that that Poppit. And and so this is what we plan to do with quantum computers. We’re not quite doing it yet because for this to work, the noise has to actually be below a certain threshold. And the reason for this is because if the qubits have are too likely to make a mistake, adding more Kupets actually makes the problem worse, even though you’re you’re trying to improve it by spreading it out among many kupets. If the error rate is too high to begin with, you’re actually making the problem worse. And so we have to wait a little bit longer to bring the error rate down a little bit before we can start doing error correction. To answer a question about the timescale, we think that it’s only three to five years before we might start to see commercial deployments. And that’s that’s not far off at all. It takes time to develop quantum application. We’re usually starting from scratch. So much so the project we’re working on now take one, two years to develop. And so that’s why companies, companies are starting to invest now. So that they’ll be in a good position to take advantage of this in a few years, they’re they’re kind of developing their muscle, so to speak, they’re becoming familiar with quantum computing and learning how this will affect their field and how they can get involved in it. And they’re taking the first steps to developing the programs so that they can take advantage of it in a few years.

**Jim: **[00:14:45] Mark, that that noise that you’re talking about is that is I used to like decoherence for the lack of coherence. Is that still kind of the the main issue or

**Mark: **[00:14:54] It is that that’s the issue. That’s the reason that quantum computers work. That’s the secret sauce is because. When I describe it, Cubitt, as being in the zero state and the one state simultaneously, this superposition. That’s possible because of coherence, and so when when we say that Chubin Quantum State or Cubitt still has coherence, it means that it’s preserving that superposition. Right. When we secure it loses its coherence. It means that the state has chosen one of those configurations. And and at the very end of the program, when you do our measurements, we’re done. We then we want it to lose coherence. Right. We’re measuring it and we’re done. But we don’t want it to lose coherence until then, because if that happens that all the special properties of quantum computing go away and it just becomes like a normal computer. And so, yes, it’s it’s preserving this coherence. That’s really the tricky parts.

**Jim: **[00:15:52] Yeah, it is. That is that getting solved by different techniques and like I think of clusters or nodes or multiple nodes types of mentality, it

**Mark: **[00:16:03] Is so these different approaches that people are using for quantum computers. So those are all focused on how we can preserve coherence as long as possible. And some. So the different techniques have different trade offs, so, for example, superconducting quantum computers, the advantage is that we we’ve been using superconducting technology for decades. So we have some experience with that. The operations are very fast, so we can do calculations in nanoseconds like billions of a second. The disadvantage is that decoherence doesn’t last very long. It’s on the order of milliseconds, right? I don’t drop technique. It’s it’s a little bit newer, it’s the advantage is that you can do it at room temperature. You don’t need to have all the special equipment to keep it near absolute zero. The coherence time is much, much longer on the order of minutes. So you can actually retain the special quantum properties for for a long, long time. The disadvantage is that the operations are slower. And it’s actually funny, it’s about the relative scale of operations here. Time is actually about the same. It’s about it a thousand times the time. It’s about a thousand times of the operation time. So, so funny coincidence. The trade off is the same. But but yeah. So so trying to get these cubits to stay coherent as long as possible is really the fundamental challenge.

**Marv: **[00:17:32] So, Mark, given the measurement and quantum physics causes disruption or loss of information, how do you get an answer out of you?

**Mark: **[00:17:42] You do want to make the measurement eventually, but only at the end of the day, when you’re when your program is done running, then you can do the you do the measurements on the qubits and you get some answers. And so so as I mentioned, every quantum program has has three basic steps. So the first step is the initialization. So you initialize your qubits. It’s usually to the zero configuration. There are exceptions, but usually we start to qubits in the zero configuration and then we do operations or manipulations. So we do procedures and we have the qubits do sometimes we do them to keep to qubits by themselves. And sometimes the Cubans are interacting with each other so they change their values around. And then at the end we do the measurements. So it’s every quantum program has those three steps, initialization operations and then the measurements we need to keep the coherence during those operations. Right. Otherwise that the special quantum properties are lost and there’s no point to doing this and so on. So, yes, this is this is what we do at the very end. The qubits are in this superposition. So the cubits are representing several different configurations. When we do the measurements, we’re forcing the qubits to choose. So when we do a measurement on a cubit, we’re forcing it to choose. Are you a zero or a one? And it will make a decision.

**Mark: **[00:19:13] So we don’t ever get to see the qubit exist in these multiple configurations. I know it it seems very science fiction when we describe all these simultaneous configurations. And mathematically that is what’s happening. But when we actually do a measurement, we only ever see one possibility. And so so we actually don’t know what the we don’t know what the outcome will be for certain. We only know the probabilities of how it’s going to come out. And that’s actually a big difference with quantum computers compared normal computers with a normal computer. If you run the same program again, you should expect the same answer unless the computer is physically broken. If you have the same input and you run the program again, you should get the same output with a quantum computer. You could get different output each time you run it and nothing is broken. That’s just how quantum physics works. You you do the same operations and manipulations and then you do the measurement and you could get different results. And so that’s why we often run the program many times, a thousand times. So we get a histogram of of what the results are like, how likely we are to get each outcome. And usually that’s the important quantity that we’re we’re going to use.

**Marv: **[00:20:27] So can we put this into a practical discussion? Let’s say you’re trying to do a material science breakthrough on some new kind of molecular structure or even a chemical one, where you trying to find a new chemistry that might help some problem? How does this application of quantum computers really solve something like that?

**Mark: **[00:20:46] Yeah, so that’s actually the that was the first example. This is actually why quantum computers were first thought of about 40 years ago. Richard Feynman, famous physicist, he was thinking about these types of problems like chemistry, and he realized that there were a lot of problems that we would never be able to solve using normal computers. And that sounds funny, like don’t we use computers to do chemistry and science all the time? We do. But there’s a limit. When you have a molecule, the larger of the molecule gets more atoms. There are and that each atom has electrons. And so as the molecule gets bigger, we’re considering more and more electrons. And we know what the equations are for, for simulating molecules. We’ve known for one hundred years the equations themselves aren’t difficult. But as the molecule gets bigger and we have more electrons and protons in the center of every interaction, there has an equation. Right. And so solving all those equations simultaneously becomes very difficult for a normal computer to solve. And this is why we can simulate only the very simplest molecules using classical computers. And Feynman realized this. He saw that this was an example of a much, much broader class of problems, that as you make the problem a little bit more difficult, that you make the molecule a little bit bigger. In this case, it becomes much more difficult to solve. And so he realized that normal computers would never be able to solve this. And we needed to develop a new type of calculator based on quantum physics, because these equations are quantum type equations. And so so he suggested we build these things like quantum computers. But of course, 40 years a. We didn’t really know how to do that, and so a lot of academic research was done and the joke was that quantum computers were always 10 years away and that was true for about thirty five years. But but about seven years ago, there were some breakthroughs that people realized that this might be commercially feasible. We might be able to actually build them and have it worthwhile to do certain problems.

**Jim: **[00:22:54] So very, very similar to directed energy.

**Mark: **[00:22:59] So. So that’s. So that’s how it works. Yeah. We know what the equations are for chemistry and we can use normal computers to solve very simple cases, but large molecules, we would never be able to do so. So for example, Tuffin, my favorite molecule, we would need a computer about a tenth the size of the Earth to simulate that. And so that’s that’s clearly not very practical. And if we wanted to do a molecule about twice as big like penicillin or something, we need a computer about the size of the universe. So so even theoretically, you couldn’t get anything bigger than that. But a quantum computer could do that pretty efficiently. So, for example, the second molecule I mentioned, we would need a quantum computer with only about three hundred cubits. And so that’s that’s not impossible. We can’t do it today, but that’s not at all impossible. And so that’s why we’re so excited when I talked about being able to solve problems that we never could have hoped to solve before. That’s a prime example.

**Marv: **[00:24:00] So, Mark, let’s cut over per second on to cybersecurity, because we’ve all heard that our encryption algorithms are going to be in jeopardy when these quantum computer can actually break down the mathematics structure of the random numbers. Where are we with that and what could be done?

**Mark: **[00:24:17] Sure. Yeah, this is I get asked this a lot, because if there’s one thing that people might have seen, it’s that they’ve seen headlines about quantum computers and hacking and threats and such. And so so first, let me explain why why that is and the truth in that. So when you. When you transmit information on the Internet, you’re usually sending some sort of encrypted version of your information. So when you take your credit card numbers in to do some online purchase, what happens is that your computer encrypts those credit card numbers. So it applies a complicated mathematical formula and that’s what’s transmitted. And then the computer on the other side will decrypt it. So it applies some inverse mathematical formula to undo it and they get your credit card number safely. So if someone eavesdrops and they they intercept that communication, they’ll just see gibberish. They won’t actually see your credit card numbers. And the mathematical formulas that are chosen to do this are purposely so complicated that it’s very difficult to directly undo that formula unless you had a right. When you when you hear about hacking nowadays, it’s it’s usually not because someone directly undid the formula, it’s because of some human error. They bribed someone to undo it or someone wrote their password down on the stick or something like that. It’s usually not that they directly did the mathematical formula. So so when these formulas were developed several decades ago, they were chosen to be complicated enough that that normal computers would never be able to undo them directly.

**Mark: **[00:25:49] Quantum computers, it turns out, are good at undoing them, and of course, they didn’t realize this at the time because computers exist about twenty five years ago. The same professor to make sure he develops a formula algorithm for how quantum computers could efficiently undo it, and this was academically interesting that this existed, but no one took it very seriously because there were no quantum computers. Now that quantum computers are becoming kind of powerful, people are starting to worry that it won’t happen tomorrow, but at some points in a few years, it might be that quantum computers could directly undo the encryption formulas that we often use. So RSA is useful example that people give. There’s other examples, too, so. So what can we do about this? So the good news is that, of course, people have been aware of this for several years and so many people have been developing alternative encryption formulas based on mathematics that we don’t think quantum computers could undo. And I want to underline that we don’t think we actually don’t know for certain that the government agency missed in Colorado. They sponsored a contest for people to submit what are called post quantum encryption algorithms. And a few years ago, the initial submissions came in and there were over one hundred and that people then did their best to defeat this. So using mathematics and computer science, people tested whether they could have a quantum computer or defeat them. And and so several were found to be vulnerable. There are now seven finalists, and we think that in the next few years, Nyst will announce one or two winners.

**Mark: **[00:27:35] So one or two will probably emerge as as being secure that that we we don’t think quantum computers could defeat them. And so so that’s the good news is that people are very aware of this. They’re already developing defenses against quantum computing. And and if you’re in a position to come to upgrade your systems security, I would encourage you to do it. Now, some people are tempted to wait like they think, well, what’s the threat? I’ll just wait until I start to hear about computers being more powerful. I wouldn’t wait until you see headlines of this actually happening because it takes time to do this upgrade if you’re in charge of a government agency or a large corporation. Of course, there’s a lot of legacy systems. It can be complicated. It can take months or even years to do that upgrade. So I would start out the second reason I wouldn’t wait is because there are rumors that that hostile, bad actors are archiving things right now. And so when they’re quantum computers become powerful enough, they will just be able to decrypt it. So this this kind of harvest now decrypt later attitude. And and that would still be bad if someone got a hold of your your health records, your bank statements today, it would still be bad news if they decrypted it a few years from now. And so so that’s why I would strongly encourage people to start up right now.

**Marv: **[00:28:59] So, Mark, I know you guys out there were at one point in time working on entangled photon quantum random number generators, which would be a different take on this problem, correct? It would be true. Random number would not be breakable by a regular computer or maybe a quantum computer. Can you tell us where that is and what’s going on?

**Mark: **[00:29:18] Sure. Sure. So whatever encryption system you’re using, whether it’s the old type or the new post quantum type encryption algorithms, you need random numbers. And and so these numbers are used for the keys to encrypt or decrypt whatever you’re doing. And. This is often overlooked, people just take for granted that random numbers are easy to come by. They I don’t know, they ask their computer for random numbers or something. They don’t give much thought to the fact that it’s actually very difficult to come up with a truly random and secure random number. And and the reason that this is subtle is because when you ask your computer for random number, a normal computer can’t do anything random. It just follows instruction. What it does is it produces a pseudo random number. So it has some formula which uses soft settings like internal settings, like the number of milliseconds since you turned your computer on or something. And it uses a formula to produce numbers which look random to us casually, but they’re not. It’s a formula and it can be predicted and it could be intercepted. And so so that’s not secure or random at all. Using quantum physics, you can produce random numbers. So, for example, the one I talked about, qubits being the superposition, if you set up a cubit so that it was in the zero configuration and half in the one configuration and then you measured it, it’s a 50 50 chance whether you’ll get a zero or one.

**Mark: **[00:30:45] And so by doing that repeatedly, you could get a random set of bits and you could turn that into a key. So that’s that’s good, but if you do that, there is no way of knowing whether someone is tampering with it or eavesdropping, right. If you would just get a string of bits, even if it were random, you would have no idea whether whether that’s true or not. There’s no security. There is a way to do this. So quantum physics also has another trick up its sleeve called entanglement. And entanglement is when the value of one cubit is correlated with the value of another. Kupets. And this is a purely cosmetic there’s no classical version of entitlements. And what you can do is you can produce entangled pair of two bits and you can then measure them independently. And as long as they were entangled, you should get identical results. Because of that, right, and so then you can just check whether you’re indeed getting the same results. And if you are one hundred percent of the time, then you can you can feel confident that. The number truly is random and secure because if anyone had tried to tamper or eavesdrop or something, they would break the entanglement before you and you aren’t guaranteed getting the same answer every time.

**Jim: **[00:32:04] That was a modern version of a public and private key. And someone.

**Mark: **[00:32:07] It’s a bit like that. Yes. And so, so so there’s this very clever technique using quantum entanglement where you can not only produce random numbers, you can prove that the random that no one knows what they are except you that you got there first. And so that’s really what you need for for generating keets. You need to have the random and you need them secure. The only way to do that is using quantum entanglement. And this is something that the U.S. has been working on for a few years. And we developed a way of doing this using quantum computers. And so there’s no service on IBM’s quantum computer cloud where you can use their quantum computer to generate these these provably secure keys.

**Marv: **[00:32:50] So, Mark, this is such a fascinating discussion, we’re already into our 30 minute normal timelines. Let me just ask you one last area to get off the stage. It probably and then I’d very much like to continue on with another session. But when you’re talking about the entangled communications, it reminds me about the satellite entanglement that the Chinese accomplished not too long ago where they had entangled the communication between Earth and the satellite, which is a phenomenal accomplishment. So I know the Chinese are competing with us for almost anything you can think of a and quantum for. For starters, what’s the status of where we are with things like that and what’s going on between China and the rest of the world and quantum computing?

**Mark: **[00:33:32] Yes, so you’re absolutely right. The same entanglement that that lets you produce these trivially secure TS. It also lets you have provably secure communication, because if anyone intercepts that entanglements or interrupts the communication, it would break the entanglement and you can detect that. And so this type of communication is called quantum key distribution. And it’s usually only used to transmit keys security because for reasons I’ll explain, there’s there’s a lot of overhead and it’s kind of slow. And so we usually don’t use it to send the communication itself. We use it to securely send the keys. And then we set up a secure communication on normal digital channels. So the Chinese did send up a satellite called the satellite, which which does this using lasers, and it has entangled photons between space and the earth. And so this is an incredible accomplishment. There is sort of an arms race in the quantum technologies area between the east and the west. And China is very aggressively pursuing quantum technologies, especially communication. This is really their wheelhouse is setting up things like this. We know that they’re outspending us at least 11 to one, and that’s just what we’re aware of. They’re probably spending more that we’re not even aware of. We the US has started to focus on this a little bit more. They’ve been given money to national labs. Now, a lot of universities have set up programs. We have several corporations I mentioned well, IBM, Google all have great programs. And Europe has a lot of programs. Obviously, we’re a British based company. Germany is getting into it a lot. The Netherlands has a very strong program. And so so there are efforts. But China. Very singlemindedly getting into that, and, of course, there’s a much closer connection between industry and the government there and the military and so so they’re basically just writing blank checks to fund lots of development.

**Marv: **[00:35:34] Fascinating, though. I just want to ask you one more question. You remind me of the A.I. arms race we have going on with China. So is there anything particular about artificial intelligence that Quantum is going to radically improve for us?

**Mark: **[00:35:48] It’s yes, there is a relationship there because I really depends on machine learning and quantum machine learning. We believe we’ll have several advantages, so one is in Monte Carlo simulation, so a lot of things when you’re simulating some sort of random process, we use Monte Carlo simulations in natural language processing. There’s actually a very nice connection between the mathematics of of natural language processing, like how we. Communicate using normal words, and then how things like Alexa and Siri understand them. There’s a very nice connection between the mathematical structure of that and quantum computing. And we actually hired a prominent professor at Oxford, Bob Cook, who was the world leader in quantum natural language processing. And so I think the next two years will see a big development in quantum A.I. from these types of things.

**Jim: **[00:36:45] Mark, I have kind of a people question. So I think recently I heard on a podcast, like most of the programmers in Silicon Valley, are leaving the I.T. community to move over to distributed finance block chain and this whole crypto crazy market. What’s it take a programmer today who wants to get into quantum computing from a from a software point of view? Does you have to learn a new language or do we have enough people to to to support this idea of going after and doing this?

**Mark: **[00:37:17] Yeah, that’s an excellent question. And I’m I’m in a good position to answer, I think, because because I had to make this transition so I can attest to what what it took. So even though I have a physics lab and I spent several years doing research. I don’t even do the research in quantum computing, I’m on the business side of the companies, and that’s because it’s not like you can just quickly go to a boot camp, learn a few things and then start writing quantum algorithms. There’s actually a lot of things that you have to learn. There’s a lot of background knowledge. And so the people that we hire to do the research tend to have degrees in specializing in quantum computing or something very similar. And then they can catch up. So when I when I started about four years ago, there were maybe 10 serious start ups in quantum computing. Now there’s hundreds. I don’t even know what they are because there’s a new one that pops up each week. But there is such a demand now for for needing people in this. We are starting to see a lot more universities have programs in quantum information sciences. That’s an academic term for this. So they offer degrees in this. There are online courses. I think MIT Expo is one. There’s sort of some online certification. I think Eibner, they call it quits kids. That’s the name of their language. They offer an online course and certification. So there’s a lot of ways that you can do this, but it does take some investments in your in your time to to do this. You do have to learn about quantum physics and and and how this works. It’s so counterintuitive compared to programming a normal computer. We just take for granted that computers actually think a lot like us quantum computers. It’s just crazy. It’s really not at all. Intuitive to develop these things, yeah.

**Marv: **[00:39:19] Thank you, Mark. It’s been a fascinating discussion. I really, really thank you for coming on and giving us all of this information. And we definitely want to get back and carry a little bit further and talk about it. I can we can actually hear the people talking about the day when the A.I. rights, the quantum equations work for algorithms. So let me thank you. And we’ll get off the stage and give you back the rest of your day so far.

**Mark: **[00:39:44] Thank you. Thank you both.