How Quantum & AI Will Shape the World’s Future w/ Jack Hidary | EP #123

29:26Can you differentiate those two? Sure, that's correct. When we look at what's been done before in trying to model biological systems as an example, a lot of good work has been done. But unfortunately, it did not involve the physics itself. And so people would look at libraries of proteins and what's fundamental to proteins as we know is their confirmation, the way that they're folded like an origami. And when you have a string of amino acids, the building blocks, the Lego blocks of proteins and you string them together, they're going to fold in a certain way. And that folding is fundamental to the use, to the application of that protein.

30:05In fact, when we have a misfolded protein, that's a whole nother ball game and that leads to diseases of all kinds. How time is? Yeah. Stan Prussner, who won the Nobel Prize and is at UCSF, an incredible body of work showed how misfolded proteins can lead to complete disaster in the brain and other organs as well. So when we think about the prediction of folding based on a string of amino acids, one can just look at lots of examples and based on those examples, train a neural net on what would happen. And that's in fact, you know, how a number of the methods that we use out there do that, like alpha -fold and others.

30:45But now we have the ability people to go beyond that. Because while alpha -fold does a very, very good job, it doesn't actually get down to the very specific ways in which it will int that protein will interact. Looking at the structure of the protein, the folding is only the first step. We must now get to the dynamics of the protein. How will it act on other things? How will other things act on it? And again, we come back to electrons. And electrons are described by quantum mechanics. And so if we want to understand how an electron on one molecule will interact with the electron on the receptor, on the target, we've got to get down to that level.

31:25And that's the level now. Finally, that we at Sandbox EQ have been able to model things at. And that is a big breakthrough. That means that that is amazing because that applies across all material science, all biology. What was that moment time? So I mean, when you joined alphabet to head this division, did you have this in mind already or was this sort of something that unfolded as the computational power came online? I mean, help me understand that moment of creation. I'm just super curious. My first area of focus was actually an AI. As you know, I was applying AI decades ago now to brain imaging as you opened up with and specifically to FMRIs to dynamic brain imaging.

32:13dynamic brain imaging as readers may as listeners may know is not the same as just a static CT image, you know, to put it on a light box, let's take a look at it. You're talking about gigabytes and gigabytes and gigabytes of data taken over a period of time. Look at blood flow of your ear. Right. Looking at blood flow, for example, where I'll give somebody a test and ask them to move their finger. If they're moving this left finger here, index finger, this, the exact spot is roughly about right here in my brain right now moving this finger, this minute right now. I can see your homunculus right now.

32:50Yeah, right there. It's right there. As we're speaking, and listening to each other, as we all know, we're using Broca's area here, number 44, to speak, and then I listen to you, Peter, I'm losing Wrenacus area back here. And so, you know, when these, When you look at these FRI images, the human eye can only see so much. And so my team and I began to train neural networks, primitive ones at the time, but neural networks nonetheless, to see if we can glean more information from these medical images, and sure enough, we were able to do that. And that shows the robustness of this idea of a brain -inspired neural network.

33:27Even with a very, very primitive compute we had at the time, building computers ourselves, literally by hand, we could actually make that work. Fast forward to today, what one thing I realize is with AI, yes, language would be important, but the quantitative world Peter would actually be as if not more important. The majority of our world is quantitative in nature. The majority of our world is governed by numbers. And so rather than spent a lot of time developing large language models, we became very interested in the quantitative models. And that took a number of forms. But then realizing, of course, from the background of physics, that we needed to figure out a way to take these quantum equations that you were just discussing, Schrodinger's equation, Heisenberg's formulation, all these interesting equations, we needed to do that at scale.

34:21And the conventional wisdom at the time, Peter, was that we would need a quantum computer to do that. We'd have to wait two or three decades to get a quantum computer as specifically a quantum computer that was fault tolerant, that was error corrected right now. Right now. Low and error rate. Exactly. You're not speaking from computers right now that are error corrected. There are literally mistakes that pop up in computers. How does that happen? Well, actually, Mewons, cosmic rays can actually hit your computer and cause a bit to flip. And so we have various error correction schemes in our phones, in our laptops, in our computers and our watches that allow for error correction for transistors for bits 0 and one type bits.

35:06It's not that hard because you know you can take you can take a vote if you want to of multiple bits and over represent the bit you want with many bits. We have I mean bits are so cheap to make why not have lots and lots of extra transistors. But in the world of quantum computers it's not that simple. These are very very sensitive devices which is one reason why by the way you flip the script and make instead of a quantum computer, you could turn it to a quantum sensor. Maybe that will leave that to another time in this podcast to talk about. But basically quantum computers are very sensitive to perturbation from the outside world.

35:44And so they do need this error correction. And that is hard. That error correction is hard, Peter. And so we knew at the time that it would take years and years before we'd have an error corrected quantum computer. Let me pause you here one second because it's a really important distinction here for everybody to understand what sandbox aq is doing because you're a software company you're not building quantum computers and there are a multitude there are dozens of companies building quantum computers and we can talk about what the horizon for those are. Yes. But the important point to make here is that you can use the power of quantum physics of the equations to understand and model molecules and such without having quantum computers.

36:29That's correct. That's the consequence of the power. By the way, Peter, when quantum computers, one day do get scaled and do get, and we encourage and we have relationships with more than a dozen of the quantum computing hardware companies out there, it'll add more fuel to our fire. And of course, Google is one of the leaders. I was just at Hartmouge Lab seeing their beautiful, beautiful goldfish and the liars. Beautiful work. And they just announced some some some great progress on reducing the error rates and quantum computers. And that's all fantastic. But I think the point here is that the same computational power in those GPUs that gave us a large language models, you've been able to build algorithms that you can use on those GPUs to approximate or to solve these you know these classical equations of Shreundjer and Neal's Borer and Plank and Heisenberg to help you model the actual world of atoms and electrons and ions today without quantum computers.

37:30That's correct. And correct is going to give us incredible insights into the physical world across health, materials, environment, everything. Peter, not, not, will get is giving us, is giving us right now. Right now, this is happening in real time. And that's what's so exciting. And when you look at, again, coming back to the fundamental idea of information, right, of what does it mean to take a part of the world and represent it in an equation, in a dynamics, in a modeling, in a simulation? You're talking about, again, it's very similar to what we did with language. We talked about large language models.

38:10We took a corpus of billions of words and we compressed it down and then bedded that information into the weights, into the weights of a neural network. Again, we didn't memorize those words. That's not what we did there. We embedded information into a space, into an information space that encodes all that stuff. We're doing something similar here. We're taking the dynamics of a certain molecule and we're describing it in a much shorter way using these equations given to us over 100 years ago by the quantum grades. And that allows us then to make predictions, very precise predictions about, okay, Peter, let's say you work at a large pharma company and you say, Jack, I heard you have this wonderful platform.

38:57I'll give you a molecule that we're thinking about that might hit this particular target in the body. Maybe it hits CleoBlastoma as an example, this brain cancer, a RIFIT disease. And we'll take that, we'll make a digital twin of that, Peter, and we'll make 10 million, 100 million, maybe a billion permutations on that drug. We'll add a methyl group that is adding a carbon and a few hydrogens. We'll add a nitrogen. We'll add an amine group. We'll add this. We'll take away that. Each one will be a slight variation on the theme that you initially started with. These are simulations. These are AI simulations in quantum.

39:36That's right. So first there, we take the quantum equations, we run those, and that becomes the data set. So we're generating our data set, and that's what we use to train the AI. Okay. Let's pause right here. That's a fundamental point. That's a fundamental point. Yeah. That is a fundamental point. Yeah. If you were trying to discover these molecules that are useful in cancer, Alzheimer's, and so forth, trying to get those with a large language model, the data doesn't exist in the corpus of data that the large language model is. That's right. It's outside the data set. Outside. It's impossible for them to discover it if they didn't have the data in the first place.

40:16That's correct. And so you've got to generate the data that these quantum models can then assimilate and generalize. Let's just double down on that for me. Yeah, let's double click on that. So basically what's happening here is instead of the world of large language models, we've now entered the world peter of large quantitative models, LQMs. And LQMs are about starting with equations to generate data. That's the most efficient way to generate data and the most accurate way to generate data is with the equations themselves. The equations are the bedrock of the universe. They are the fundamentals.

40:56They're upon which everything is built and created. That's correct. The fact that humans, by the way, just taking a step back. And the fact that human beings have uncovered the quantum equations of the universe is stupendous. Deserves a moment of silence. Okay, moment to observe. And so this is absolutely incredible. And by the way, as many of our viewers may know, the quantum equations are not one of their the most tested set of equations that we've ever had in the corpus of science. And they were so, and they, people wanted to doubt them so much. They were so much. Even Einstein. Einstein hated them.

41:39He hated them. God doesn't play dice. He hated them. He railed against them until his death in the 1955, because although he was one of the careers of it, he couldn't comprehend given a classical view that he held onto how this world could even be described by these equations. Ironically, one of his best known papers, 1935, known as EPR Einstein -Pedalski -Rosen, the three authors of it, was an attempt to derail quantum mechanics as a science. It ended up becoming a cornerstone of the science, describing the phenomenon of entanglement. But coming back to your key question, Peter, which is, what's happening now in the world of quantitative AI, rather than having to use a corpus of data on the outside world, which contains a huge amount of garbage and false info and good info, all mixed together, like in the language world, we start with the pristine equations themselves.

42:39We take a theme and make variations on that theme. We take a molecule, make variations on it, we take a battery chemistry, which we're dealing with ions now, and ions again are subject to the quantum laws and we're saying, okay, this is a lithium ion battery, but we've been stuck with lithium ion chemistry Peter for 40 plus years, right? And we actually need to kind of start moving beyond that, need to think about what other chemistries would give us batteries that may be cheaper, maybe more lightweight. The heaviest thing in a car in electric vehicle is the battery, but batteries actually are going way beyond just electric vehicles.

43:13The bigger market, the bigger application for batteries is not in cars. It's stationary. It's in every building in the world needs to ultimately have an energy storage system that accepts electrons when they're cheaply available and then uses them when they're in demand. And that arbitrage, that day trader like arbitrage of buy, win, low, and use, win, high, right? That is going to impact the world of energy beyond anything we've ever seen. I'll tell you the one I'm waiting for is room temperature superconducting. That's what I want to call them to deliver us. Yes. Well, that's the kind of modeling we now are beginning to embark on.

43:56So when we think about the drugs that we need, the medicines that we need, we think about diagnostics biomarkers that we want to have right now, as our audience may know, there is no marker in the world for the progression of Parkinson's. There's a marker that tells you whether you have Parkinson's or not, not very helpful, since it's probably very obvious. But in terms of whether it's progressing or you've halted it with some treatment, there is no. So we need new biomarkers. We need treatments. We need better battery chemistry. We need cheaper solar energy. As cheap as it's become, the fact now that the underlying substrate of solar silicon is competing with the semiconductor industry, it does not bode well for the inexpensive nature But perovskites coming.

44:42Perovskites are exciting, but what's the problem? Perovskites, Peter, they're not stable. To be bankable, to be financeable, solar panels need to be, have a 25 year guarantee, a 25 year life shelf stable, roof stable. In fact, perovskite technology only takes us out about a year in terms of stability. And so there's a need to actually model that at the quantum level. There is a company, Paranovo. I'll tell you about it sometime soon. That's doing a heck of a lot better. We hope so. We want that to happen. We want that future. Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love.

45:21Companies called Fountain Life. It's a company I started years ago with Tony Robbins and a group of very talented physicians. Most of us don't actually know what's going on inside our body. We're all optimists. Until that day, when you have a pain in your side, you go to the physician and they Berncy Room and they say, listen, I'm sorry to tell you this, but you have this stage three or four going on. And you know, it didn't start that morning. It probably was a problem that's been going on for some time, but because we never look, we don't find out. So what we built at Fountain Life was the world's most advanced diagnostic centers.

46:00We have four across the US today and we're building 20 around the world. These centers give you a full body MRI, a brain, a brain vasculature, an AI -nabled coronary CT looking for soft plaque, dexascan, a grail blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AI's and our physicians to find any disease at the very beginning when it's solvable. You're going to find out eventually. Might as well find out when you can take action. Found life also has an entire side of therapeutics. We look around the world for the most advanced therapeutics that can add 10, 20 healthy years to your life.

46:43And we provide them to you at our centers. So if this is of interest to you, please go and check it out. Go to foundlif .com -peter. When Tony and I wrote our New York Times bestseller Life Force, we had 30 ,000 people who reached out to us for Fountain Life memberships. If you go to FountainLife .com, Backslash Peter, we'll put you to the top of the list. It really is something that is, for me, one of the most important things I offer my entire family, the CEOs of my companies, my friends, it's a chance to really add decades onto our healthy life spans. Go to FountainLife .com, Backslash Peter, it's one of the most important things I can offer to you as one of my listeners.

47:30All right, let's go back to our episode. So back to the core question that you have. The fundamental breakthrough now of realizing that what's happening in the world of physics, in this case quantum mechanics is that we're summarizing essentially a massive dynamics in the universe with these core equations. We're taking a molecule that has infinite degrees of freedom. It can move in any way, form or manner. there's anything that can happen to it. And then we're focusing like a laser on the business end of that molecule. We're not going to model every electron in that model, in that molecule. We're going to limit ourselves to the valence electrons, that is the outer electrons.

48:12And within the valence electrons, we're going to limit ourselves to the business end of the molecule that might be hitting the actual target that we're going for in the body by could strain ourselves down to that portion of the problem, we can make it tractable in today's GPU based computers. When did this become possible, Jack? When did it become possible for you to do this with the compute and the algorithms? We first had the breakthrough. We had the first breakthrough exactly three years ago, just three years ago. Yeah. So just three years ago is when we realized this is going to change the world.

48:49This is something that's going to fundamentally change how we do things. And again, well, most of the world was focused on language. And again, God bless the applications for college students, writing essays, an hour before the deadline for language models. But we realized that this was going to be a fundamental change. And one, how we thought about AI and how we thought about the use of quantum in the real world. We've always had quantum and textbooks. We have many quantum innovations. The MRI machine is a quantum in nature. The laser is quantum in nature. Lots of quantum. We've used them, but we haven't been able to really predict and utilize them.

49:30I don't know if the strength is that scale. Yeah, that scale on any arbitrary quantum system. And that's now where we've come to. And that means that the world now has a superpower down. A new superpower. Humans have a superpower. This generation of humans is the first to have the superpower. to do this at scale on real world types of systems. Every quantum textbook in the world usually has a chapter one, two, and three describing the equations. You just rattled off Peter and then has a chapter five or six that says, let's use it on an actual case. And what is the case? One hydrogen atom, one proton, one electron.

50:10Now, as far as I know, hard to cure cancer with just an atom of hydrogen. And so we got to get to real world system and that's what happened. In the last number of years, our team and I, we've worked on real molecules from labs and UCSF, Nobel Prize winning labs. We've worked on molecules from large farmer companies, spinouts, all kinds of folks working on battery chemistry with a company called Navondix, a public company that does battery chemistry, working on new materials for the US Army. The US Army wants to the lightweight, the tanks, hard companies that may announce soon want to lightweight their vehicle so they're more fuel efficient.

50:50Well, that's new material science. We need new materials to make that happen. Materials are made of atoms, and those atoms have those electrons that we talked about. And so we've got to fundamentally rethink now. By the way, I think I would - Material science is like the most under -appreciated area of technology, right? everything that is new and breakthrough, I bow down to material scientists and the work that they do. And this notion of the materials genome, right, the idea that we understand the fundamentals of certain limited number, like, you know, fraction of a fraction of 1 % of materials that are possible.

51:30Right. And we use them. But given what the work that you're doing were able to expand this understanding that will head towards, you know, fundamental across every industry is going to be transformed by this. Yeah, and Peter, you get into a fundamental point here, which is the compute we're talking about now, both AI, quantum, this quantitative AI we're talking about. It's not just about doing things faster. Often people write, oh, they're doing things faster. And by the way, Faster is good. I mean, yes, let's get to the medicine faster. That's great. But here's the more fundamental point. We're actually exploring a bigger landscape.

52:12We're able, in the case of medicine to explore a bigger biochemical space than can otherwise be explored. In the case of material science, explore a bigger space. If you're looking at battery, uh, battery chemistry, there's about 19 elements of the periodic table that you could in different combinations build a battery from the, for the electrolyte, for the ad note, cathode for the membrane, all these core four elements. And so we actually can now start to explore a much bigger space. If we were limited before, as we were, to building prototypes by hand and testing each one, how many could you possibly build, right?

52:49Even if you're Willy Wonka and I have Oopalumpas around, you're limited to the number of batteries you could possibly prototype. But now, now that we have the actual quantum equations in the system, and you could run it at scale. You could explore a much bigger material science landscape. Gone are the local minimum and maximum that we've been living with. Yeah, we were really in a cul -de -sac to use a more suburban term for a minimum or maximum, but yes, yes, exactly right. So let's talk about what the implications of this are. Our favorite subject, you and I both love health and longevity, and reminding people the way that we've discovered drugs.

53:33In the past, we'd go into the Amazon, we'd chop down bushes and trees and dig up dirt and we'd crush them up and we'd try and find unique molecules and we'd test them a molecular at a time and that led to today's devastating drug industry which is riddled with failures. What do you hold as the average drug development time and cost a decade and $3 billion? Exactly. Right now, it's about seven to ten years of preclinical work that is developing first a target. You got to start with the target and the body. What are you going for? You've got to validate that target and then you've got to drug that target.

54:10You've got to design a drug that fits like lock and key into that target. That's about eight to ten years. Then you go into clinic, even with and God bless. The FDA has actually done a pretty good job trying to compress down the phase one, phase two, phase three trials. you often could do a pivotal phase two now, where the phase two becomes the, in a sense, like the phase three. It's sufficient data to give you an approval by the idea. Yeah, to get out there. There's breakthrough pathways for drugs now, particularly for orphan drugs and, sorry, particularly for orphan diseases and rare diseases.

54:44And so when we think about the time it takes eight to 10 years preclinical, four or five years minimum in clinic, and here's the kicker. Here's the most sobering of the statistics, 90 % failure today in clinic. 90 out of 100 drugs that go into clinical phase one trial phase two phase three, never see the light of day, never come out again. You know what equally sobering for me, when you get prescribed a drug because you have a particular problem, chronic disease, whatever it might be, you expect that that drug works for you. But do you happen to know what percentage of people that drug were to prescribe for them actually works?

55:28Please. It's like 20%. Wow. The fact of matter is the FDA is making sure it's not harmful, right? That's the results we get out of a phase one, phase two side of the equation. But the FDA is approving a drug if it helps a sufficient number of people, not everybody. And so, you know, I think it's insane. But the hope now is not a drug that statistically might work for enough people for the FDA to approve it. But I want a drug that works specifically for me. I want a drug that is coded for my molecular design and genetics and so forth. And Peter, this is now the precipice of where we're going. because because of that $2 .5, $3 .5 billion for a drug program, drug companies, biotech companies have not had the ability to do more than just one big cannon shot and just hope it works and hope it goes to enough population to then amortize and pay back the cost of that.

56:30Which is why the drug costs are so extraordinarily expensive. Because also the successes, the few successes have to pay for all the failures. But now let's talk about a world. Let's talk about a world where it costs one tenth the amount to make a drug, 300 million instead of three billion. Let's say it costs half the time to make that drug. Let's say we take the rate of success in clinical trial from 10 % to 50%, 60%. This is all possible now. And so if we look at that world, then a biotech company, a lab, a set of researchers can say, you know what? We can actually now make a drug that's targeted to this very specific sliver of the demographic of the population that has this particular genome sequencing or other characteristics.

57:19And we can start to get to that future that you just described that says, instead of having one size fits all kind of drugs that end up not fitting many people at all, we can now start to really understand how we can make these more targeted drugs that really match well with key cohorts out there in the population. Now, we've been doing that in part, right? So we've got companies like Encyclical Medicine. We've got Alpha Prodo. But again, they're using classical computers and AI large, you know, modeling. How far are you from using the equations of quantum physics through the power of sandbox AQ to help companies with this?

58:05Peter, two fundamental parts of that question. We're doing it right now. And fundamental to our success and our velocity in this is that we are not a biotech company. That's fundamental. When you look at the landscape and you say, who else is using computations to help with drug design? Actually, there's quite a few companies using computation. But here's the thing Peter, they're all biotech companies. They're all companies themselves who have the burden, the overhead of trying to bring a drug to market or pipelines and clinical expenses and all that overhead, not just the money, but of time and management attention.

58:42We do none of that. We focus just on doing this advanced set of calculations in partnership with labs and with drug companies. So you're a software company that is supporting a multitude of different industries. Right. Exactly. And so that allows us to now have the world class talent that can do this. We have quantum physicists on board chemists, medicinal chemists, biologists, doctors, physicians advising us. We have all these specialties all coming together, AI specialists of all kinds, modelers of all kinds, mathematicians in our midst coming together to make this happen. That is not something that a drug company can really do because they've really got to hit their bottom line, which is making the final drug.

59:30We focus on working with dozens and ultimately hundreds and thousands of drug companies in In the same way that Oracle is a database used by every vertical out there, ultimately our platform will be used by many, many verticals out there today. It's being used by the biopharma community. It's being used by the chemicals community, by Dow chemical, by others. And then we'll start moving into material science, batteries, energy, all these different areas as well. So business model really counts. And when you want to focus and say Peter, what are you going to do be the best at in the world? What are you going to be the best at in the world?

1:00:08What is your company going to say this is our territory? This is where we're going to really focus. You have to make a choice. You can't be all things to all people. And so we made the choice to be best at this kind of computation, this software, and our software runs today on the GPUs. and it's architected so that it could also run on the quantum computers of tomorrow. And that is the future we're heading to Peter. We're heading to a meshed hybrid cloud world that meshes CPU as a basic controller GPU workhorse, absolute workhorse with QPU quantum processing unit and bringing those together is a core part of that future.

1:00:52Real quick, I've been getting the most unusual compliment lately on my skin. Truth is, I use a lotion every morning and every night religiously called one skin. It was developed by four PhD women who determined a 10 amino acid sequence that is a syndolytic that kills senile cells in your skin. And this literally reverses the age of your skin. And I think it's one of the most incredible products. I use it all the time. If you're interested, check out the show notes. I've asked my team to link to it below. Alright, let's get back to the episode. Jack talked about the future of clinical trials here because I think I've heard you sort of vision here this notion of running in silico human trials that drops the cost, not by 50%, but basically a thousandfold eventually, so that you know when you introduce this drug into humans because it's run in quantum models because it's run in simulation that you know it's going to work.

1:01:56Just like the first time, you know, the team at SpaceX launched the Dragon Falcon combination to the space station, they didn't kind of hope it would actually get there and dock accurately. They could run the simulation so many times in such accuracy that they knew it was going to work. So is that the future for drug development? Yeah, we still have to do the clinical trials. There's no way around that. But as you pointed out, we can go into the exam with the answers, right? That's very exciting. And so if we can go in, right now, the 90 % failure, what does that tell us, Peter, that 90 % failure of clinical trials today, it tells us, yeah, the way this is having gone through phase one and phase two, Yes.

1:02:40And failing in phase three, which is insane, right? So you've had enough of a success in phase one and phase two. And you know, to show somebody in efficacy, but then at the end of the day, it didn't help enough people. Exactly. So, so given that 90 % what that tells you is that we have the ability to add a lot of value here, right? Is, you know, we're not talking about some, I've to might still invest in the company, buddy. Exactly. There's a lot of value to be created here. If you look at highly optimized systems in our world, those are different stories, right? If you look at, you know, a sterling engine, one of the most efficient, you know, things on this planet, not a lot of value you can add to a sterling engine, right?

1:03:24But if you look at the clinical trials that you're pointing to, this is where we can add a lot of value by modeling exactly what's going to happen and adding to that model every year, more and more of the variables. And so initially looking at those valence electrons and how they're going to hit there and then looking at maybe some dynamics. If it's a protein, let's say you're talking about a small molecule, less than a thousand Dalton, with carbons, they being 12 Dalton. So small molecules, as you know, Peter, but to share with our listeners are things like aspirin or Tylenol or things like that are small molecules.

1:03:58But when they interface with a much larger beast, like a biologic such as a protein or an antibody or things like this, then there's a lot of stuff going on. And so we're now adding functionality to the system that allows us to do that kind of stuff, protein against protein, small molecule with proteins. This is complex stuff and it's getting more and more capable every few months. And so this is the kind of work that will ultimately lead us to a much better sense of the answer before we walk into that clinical trial. Let me emphasize, we'll still need the clinical trial because we do want to have that final real world confirmation, but we'll have so much information and modeling before that that will move up that success rate very, very dramatically.

1:04:45You know, I need to dive into quantum computing a little bit with you because while you're on a rocket ship, when we add quantum computing to your rocket ship, it becomes a warp drive and you're a starship, all of a sudden, not just interplanetary. You go into the stars. So quantum computers have been around for a little bit. And as you said, we've got dozens of companies and they're, they're qubits, they're equivalent of their bits are atoms or photons or ions, lots of different approaches. Can we talk about where they are today, your estimate, and where they would need to get to, to be functional for sandbox, AQ to use?

1:05:30Yeah, great question. And you talk about the bid around for a little while. Let's be more specific. Paul Benioff, yes, a cousin of Mark Benioff. Paul Benioff, want to give him some kudos, that was Paul Benioff. He is the one who's had the first paper describing a quantum computer. That was in 1979. He's passed away recently just in the last few years, but I want to give credit out there because often History is a quantum computing gloss over him and talk about Richard Feynman. Yes, Feynman did popularize the idea of what a quantum Computer can do and we owe that to Feynman for helping get the idea out But I do want to give some credit to a creditist deal with Paul Benioff for having kicked us off And one interesting history of science question is Johnny von Neumann, we mentioned it before in von Neumann architecture is John, Johnny von Neumann being at the nexus of computing and physics.

1:06:25Why was it he the one to conceive of a quantum computer? It's an interesting question. Maybe another session will come back to that. Yeah, we're still going to come back to a science science five papers. Yes, we're going to come back to that as well. So we have many. It's good to give answers on this podcast, but also to plant questions with the audience. as well. So back to quantum computers now. So you ask about the different modalities of quantum computers. Yes, indeed. There's seven major ways to build a quantum computer. And as you point it out, we can build them either with natural qubits or synthetic qubits.

1:07:03A natural qubit, an example, there would be a neutral atom. That's one of the dark horses in this race, one that hasn't God is much attention, but is scaling very rapidly. This is where you take a neutral atom, not an ion. So it's not an ion trap computer, but it's neutral. It's not, doesn't have any charge. You manipulate it with lasers, several people, one Nobel Prize is such a Steve II and others, for showing us how to manipulate various entities with, with lasers. We use the new absolute zero. Exactly. We use those techniques to set the atom, which now becomes the qubit into a certain state.

1:07:45And again, let's remember that quantum bits or short qubits can be in the state of zero or one, just like the transistor, can be in zero, one, a bit, can be zero, one, but they can also be in superpositions and combinations of zero and one. And that gives us an infinite palette to draw from. And that qubit we can say is going to be some part zero, some part one, or a third this, two thirds that, we can have different combinations in between. Everything in between. And so we represent these qubits in very different ways than just the normal transistor. And so that neutral add and we can manipulate into one of those states.

1:08:25We could read that state and then we can operate on that state. And that's what's critical to what quantum computer, the ability to initiate a state on a cubit, operate a set of operations on those states, and then at the very end, read it out to us at the very end. And so those are critical things in a quantum computer. And now, in fact, neutral atom quantum computers are scaling faster than almost every other kind of quantum computer. It doesn't mean the other ones are out, but advantages to neutral atoms are that they're basically room temperature, easy to transport, quite compact. And you're starting with a compartment of gas, let's say, rubidium as an example, where you already have hundreds of millions of these neutral atoms in the actual container.

1:09:16And to do things that are useful with quantum computers, we generally know that we're going to need to do the error correction. Let's use for the sake of this conversation, a ratio of a thousand to one, a thousand physical qubits to one error corrected or logical qubit, right? A thousand to one ratio. Really, really important for folks to recognize, because you hear about all these quantum computers that have 100 qubits, 100 qubits. Those are physical qubits. Those are physical qubits. Exactly, Peter. Right. They're not the functional error corrected qubits. And when you, so where are we today in this race?

1:09:53So today we're at, you know, a few hundred of these physical cubits really, some papers have claimed to make one logical cubit, but we're really not at the point of having a set of logical cubits of error -corrected cubits that we can really manipulate at this time. Now that will change very rapidly. If you look at the photonics side, using photonics, that's another promising approach exemplified by psych quantum, both in California and Australia, and also photonic, a company in Canada, as well as a number of labs working on photonics. The Chinese, by the way, are making good progress as well in photonics.

1:10:36Panjianwei, the leader of the quantum program in China, himself is a photonics -oriented physicist. And so that was the first quantum computer. My favorite science fiction stories are always about massive quantum computers buried under Beijing that brought about AI superintelligence. Yeah. Yeah. Well, there are quantum computers deep, deep inside these universities that are run by Panjongwei, but they're not in Beijing. They're about two hour fast train ride from Shanghai in a different place. But yes, they are there. But in any case, So, you have photonics. And one of the advantages that the photonics people will tell you is that we can mass produce these qubits using silicon photonics.

1:11:24We can use some of the same techniques of the semiconductor industry. In fact, cyclontum uses global foundries. Uses one of the big fabs out there to mass produce this by hopefully the millions. And so, if you want to do something like crack RSA, right, let's say you want to crack the encryption protocols that are used throughout the world that are the bedrock of our economy. The reason why we have a hundred my Bitcoin wallet and yes, that's what we want to do. We're going to crack right in and break the chain. And in fact, you want to do that with either RSA, which has been around since 1978, since RS and A, Revescha Mir and Adelman, gave us RSA.

1:12:04if you want to crack ECC, elliptic curve cryptography. So blockchain, you mentioned blockchain, Bitcoin, Ethereum, these are all based on either RSA or ECC. If you want to crack those, but more even bigger than just blockchain, every ATM transaction, every wire transfer, every e -commerce using a credit card on Amazon, every single transaction, every WhatsApp. When you're on WhatsApp, it says encrypted end -to -end. On the WhatsApp messages there. What is that encryption? That is RSA and ECC. So if you want to correct that estimates are, we'll need roughly 5 ,000 or so logical qubits. Maybe we can get away with 4 ,200, but let's just say 5 ,000 error corrected qubits, which using our 1 ,000 to 1 ratio, Peter, let's go back then and say 5 million physical qubits.

1:12:57So we're at a few hundred physical qubits today. And we're getting the five million of these things. So Jack, I know you're a betting man. And you predict the future. Actually, you implement and create the future. When are we going to get there? When do you think we're going to have actual quantum computers that you're going to want to use at sandbox AQ? I would say that. Min Max. Yeah, I would say that by year 2029, which is very, in a totally five years now, we'll start having the building blocks of about a thousand to five thousand physical cubits in like these modular Lego blocks. And then we'll start to happen as people will daisy chain these blocks up using fiber optic connection and modulators that allow the physical instantiation of those cubits inside the block to be coordinated with a quantum state without collapse, without observation, without collapse with the quantum state in the adjacent Lego block.

1:14:03When you could start to daisy chain them all together and then thus create a mega computer made of lots of these Lego blocks. Let's say for example, they each had a thousand of these physical qubits and then I got a thousand of these Lego blocks together. Now I've got a million qubits and therefore I have a 1000 logical qubits. And so amazing. Now of course, 2029 is when Ray's predicting, you know, whatever AGI is. And of course, the reality is, you know, we're going to be using some variations of digital supercomputing to help us build these quantum computers. And then they'll, you know, those digital supercomputer digital AIs, super AIs will become resident on these.

1:14:49It's an exciting five years ahead. And Peter, just to finish. So that'll be like, that'll take us about five years, then I'll take another two, three years of the engineering to put all that together, make it error corrected, bring it all in. So let's talk about the year, maybe 2031, 2032. I think it's going to be a very critical year. Yeah. Insane. And, and, and, and, and Peter, we should note, I think we should note that while we're talking about quantum computers, there's whole worlds of quantum technology to take us beyond on computing, quantum sensing is one of those critical things. Quantum senses are here today.

1:15:27We don't need error correction. We don't need millions of qubits. They're here today. They're flying on planes right now, helping us to navigate when GPS is jammed, when GPS is denied by countries and by bad actors. They're right now being tested in hospitals to diagnose how your heart is, given the magnetic field of the heart, that is magnetocardiography MCG. And of course, ECG. So these are all the areas that sandbox HQ is pioneering. That's right. Which we're going to need to come back and do it in an individual box. It's absolutely because it's extraordinary. I mean, quantum computers at this level in the next five, six years change the game.

1:16:11I mean, people feel like the world is going rapidly and disrupting and reinventing today with generative AI. I'm this is just the beginning. Peter, just the beginning. This is a, this is a super exponential on steroids. I have no, I have enough, not enough superlatives to explain how fast. Peter, let's come back if we could in summary to one of the core points. I hope we can have viewers and listeners take away from our conversation today, which is information. Information, not in a generic sense, but in a very specific technical understanding of that word. The way that Claude Shannon understood that word, the way we understand it in a field, we call QIS, quantum information sciences.

1:16:55The way we understand information now in neural networks, we're taking a large body of data and we're representing it by a smaller amount of bits. learning. And that learning, that generalization leads to information that represents that larger data set that we started with in the first place. The same in physics, where we can take dynamics in the world, be it Newtonian with the rocket ship, quantum electrons, molecules, and we can take very complex behavior and dynamics and summarize it in a small number of pieces of information called equations, called dynamics. And this fundamental ability of humans to search and look for summarization, conciseness, compactness, compression.

1:17:52This is fundamental to the breakthroughs that we're now seeing both in the AI world, and we are now seeing in the application of quantum physics for the first time at scale in compute on GPUs that we've never seen before. This fundamental insight that information is the building block of our universe. This fundamental insight that information in this sense of called Shannon's entropy of information, and we can then quantize that into the quantum theory of information. Maybe in another podcast, we'll have time to talk about that. This is fundamental to that of the human race. We'll completely revolutionize our existence on this planet and hopefully other planets as well.

1:18:39I know. And beautifully put, before we break away here, I have two questions. The first simulation theory. Yes or no? Are we living in a simulation, buddy? Let me say this, that if we are in a simulation, then the beings who created the simulation kudos to them. They've done a pretty good job. I'll put it that way. Okay. Okay. Second. And then we should also answer the Einstein question. That's the second question. Okay. There you go. So it turns out Einstein as a young man read a book by Henri Juan Corre, the mathematician and physicist that in that book, Juan Corre put out a series of challenges, challenges about Brownian motion, challenges about light, challenges about how the world works, And it turns out that four or five of those challenges are the ones that Einstein decided to tackle as a 20 something sitting in burn, being a third class patent clerk at the patent office, the only job he can get due to his friends dad who got him the job.

1:19:54This is what he decided to tackle, but he was inspired by this book. And for some reason, histories of Einstein often lost over why he wrote this on subject matters that seem to have nothing to do with each other in that year of 1905. So we have unrepunked race. So just like David Hilbert did in the year 1900, unrepunked in his book, very often contributions to our society can take the form of not just the answers, but the questions. evidence. David Hilbert put out a challenge in the year 1900 of key mathematical problems. Some of which still vex us today. The Clay Mathematics prizes more recently do the same thing, but updated them for our mathematics of the last few decades.

1:20:41On Reponkare, put that challenge out there and a young man called Einstein took up that challenge. So I leave the listeners with this, Peter, what are the questions we want to pose to ourselves as challenges to our colleagues around the world, to young people today, to our kids, to the next generation. Let's focus on the questions and not just the answers. I love it. Ladies and gentlemen, none other than Jack Hitory. Jack, you are an extraordinary entrepreneur. I am so blessed to call you a friend. Thank you for all the work that you do. Thank you, my friend. Thank you, Peter. Great to see you.

1:21:18Love you. Yeah, great to see you. Love you too. And let's do this again real soon. Take care.