Jack Hidary on Quantum Information Sciences, Grand Challenges and Moonshots

Living a life in full is a conversation you always wanted to have with that person who gave an amazing TED Talk or the author of one of your favorite books or that inspirational Olympian you always wanted to know more about. It's graduate level conversations with those making a difference in the world and in the lives of others. This show brings you new ideas and approaches so that you can live a life in full. I'm your host, Dr. Chris Stout and I hope you enjoy this episode. This episode is brought to you by the nonprofit I founded, the center for Global Initiatives. The center is an all volunteer organization so there are no salaries and thus all tax deductible donations can go to the work. While our key country partner is in Tanzania, our work is global and generally focused in the areas of education and healthcare. We also help others to get their nonprofits started or augment others project based initiatives. It's our goal to open source humanitarian intervention and to help make it easier for others to do more good in the world. We are proud to have been ranked a great non profit every year since 2011 and to have achieved a Platinum level rating by Guidestar Links in this episode's show, notes will take you to published articles on our outcomes, a number of helpful tools and downloadable resources which are all free always. Please Visit us at CenterForGlobalInitiatives.org and be sure to watch our video at Patreon.com DrChrisTout to learn more. Thanks welcome to another episode of Living a Life in Full. I'm your host Dr. Chris Stout. I'm very happy to have Jack Hittery on this episode. You may recognize him as a successful serial technology entrepreneur as he's a regular guest on Bloomberg, Fox Business and CNBC and a frequent keynote speaker having presented at the business schools of Yale and Columbia and at TEDx. Jack and I first connected via the World Economic Forum in Davos when we were both global leaders of tomorrow. In 1995, Jack established Earth Web and led the company through its IPO as Chairman and CEO. Three years later, when which by the way was one of the largest first day openings in history, Earth Day then acquired DICE and changed the company's name to dice.com and is now listed on the NYSE. He also co founded Vista Research in 2001 as an independent financial research company serving institutional investors. Jack sold Vista to the Standard and Poor's Division of McGraw Hill in April of 05. Academically, Jack studied philosophy and neuroscience at Columbia University and was awarded a Stanley Fellowship and Clinical Neuroscience at The National Institutes of Health. Under the fellowship, he conducted research in functional neuroimaging to study brain and disease states. Currently, Jack is also an investor in Primary Insight, which we'll discuss. He's a trustee of the X Prize foundation and co founder of Auto xprize, which inspired the development of highly fuel efficient vehicles. And believe it or not, in 2013, he ran for the mayor of New York City. The New York Times described him as socially progressive, fiscally reserved and digitally savvy. He's committed to community and philanthropic causes and established the Hickory foundation which focuses on medical research and oncology. He's a senior advisor to Alphabet X Labs, the advanced innovation lab of Alphabet Google, where he focuses on AI and quantum technologies. And we'll do a deep dive into his book, Quantum Computing An Applied Approach. Hey, Jack, welcome to the show.

Hey, Chris, great to be here. Thanks for having me.

Sure. And just as a side note for listeners, this has not been any easy conversation. Jack is just doing an awesome job of connecting. We were trying to do it from a airport before he headed to Europe and now he's just checked into his hotel in Germany before giving a talk this weekend. So glad we could make it happen. Glad to have you on. I want to.

Yeah, Chris, the book tour. The book tour has been great. It's been great to be on the road and connect with so many different audiences. So really glad to be with you today, Chris. Go ahead.

Well, good. Yeah. And you have been kind of hitherto and yon, before we get into it, where, where all of you been? I think I've been trying to keep track of you with both coasts and everywhere else. What's, what's the tour been like? Let's start with that.

Well, it's been, it's been really, really fascinating because technology, as you know, Chris, today is a global phenomenon. It doesn't, it doesn't stop at any border. It crosses all borders. And people around the world in all countries are very interested in their future, how the future technologies will impact their lives. Businesses are interested in how it impact their industry, their sectors. So there's great interest and it's global in nature. And I get comments, emails, suggestions, input from people from all around the world. So it's been great to go on the road and connect with people. This week I happen to be here in Europe, but also connecting with people in other continents. And this will continue for months to come because there's such great interest right now, particularly when it comes to these quantum technologies that I know we'll talk About a little later in this podcast, there's great interest because it has so much impact on so many sectors.

Yeah, the aspect of technology obviously is being pretty ubiquitous. And I think in particular these days, you know, the augmented realities and machine learning and artificial intelligence and adaptive learning kinds of things and predictive analytics and a variety of different kinds of applications in so many different industries. And now really kind of, again, I'm kind of an armchair, you know, viewer of these kinds of things these days. But the whole aspect of quantum computing. And in your book you quote, Richard Feynman is saying that nature itself is quantum. And I think you aptly titled your book that. It's your quantum computing and applied approach. And it very much is. So I have my own hot copy and I've been diligently trying to work through it. For those of you that are yet to be familiar with it, it's really. Correct me if I get any of these facts wrong, Jack, but like for STEM majors or physics or grad students, that kind of is an augmentation of Mike and Ike's textbook on quantum computing and quantum information or computer science grad students or primer for non quantum computing experts and like software engineers or physicists or other folks. And. And you've actually structured it where it can not only be a classroom textbook, but also for. For independent study, right?

That's correct, Chris. I think there's a sea change happening right now in education. I think you're absolutely correct that this textbook is being used and can be used in coursework. It's being used by a number of, quite a number of universities around the world. I'm hearing from more universities all the time who are using it as their core textbook. And as you pointed out, it crosses discipline. So quantum computing is now being taught in physics departments, it's being taught in computer science departments. Math departments are getting in on the game. Chemistry departments are getting in on the game. So there's a lot of interest from many different disciplines in the university setting. But as you point out, there's also that independent study person. Person who is not in a formal program right now. They're an engineer at a large tech company. They're a startup founder. They're somebody who is interested in maybe crossing fields and changing careers. And the revolution in education right now that I'm seeing is that people are taking control of their own education game plan and roadmap. And it used to be the case five or 10 years ago that if you wanted to get into a certain career, you'd have to have, quote, credentials you'd have to go and get that degree, that master's degree, that course degree from a well known university and only then would you be considered by a company to hire in that space. But that's really changing now. And what we're finding is a lot of people are using a book like this as they're on ramp into this field on an independent basis. And you know, you had, for example, in the last five, six years the rise of Khan Academy. You had the rise of hundreds of websites that you can learn math from physics, from science, from so many different skill sets, from coding, of course. Codecademy is a great example of an online site where people can learn how to code in Python and JavaScript and in other languages. And so people are really changing their philosophy and companies are changing the philosophy on the upskilling roadmap. And we're finding a lot of people are using the book in that context as well. So I think that's really exciting that people don't have to be limited, Chris, to just going to formal programs. And I'm happy it's being used in formal programs, but I'm also really happy that people are taking control and initiative of their own educational roadmap and saying, you know what, I'm not a physics expert, but I want to tackle this book because I want to know about the future of this kind of computer. And the good news is the way I've written the book, you don't have to be a physics expert or any kind of, you know, deep science expert. You can jump in and get the practical knowledge you need to get going in this area.

Well, I would really like to emphasize that Jack. I mean you really did, I think a lot of the MOOCs and things like that. I just took an Ed Harvard class on predictive analytics and it was just very well done. But I think your book almost kind of as the springboard into it, could be a way to bring down some of the anxieties that someone might have before they get all committed to an online course or something. I did, by virtue of your book, I signed up for the GitHub and I'm not going to publicly say how my ossified algebraic skills are from chapter 11, but it's that kind of a thing. I have never in all my academic kicking arounds seen a book put together in the way that you've done it on what can be a very, on one hand, kind of engaging and stimulating. And what's this kind of book as you had mentioned your experience last night at the airport to being adopted through major universities to be able to do that. So I want to circle back to the book, but I want to get a little bit of context for this. I just have to ask, man, how do you go from philosophy and neuroscience at Columbia to startup founder and serial entrepreneur extraordinaire, to quantum computing, and then a textbook on it, no less. What's. What's that path like?

Well, you know, in a way, Chris, I see all these as really connected. They are in different disciplines, they are in different areas and spheres. But to me, it's all part of the drive and curiosity that I have, and I think a lot of people have about the world, about the future, and that's what really drives me. I'm just always fascinated by how technology will impact our lives. What kind of really hard problems can we solve with technology? If you think about, you know, let's talk about medicine as an example, the average time from the beginning to market of a new compound by pharmaceutical companies is about 15 to 17 years and well over a billion, billion and a half dollars. And a lot of that time is pre phase one trials. And a lot of that time is in chemical development, development of the chemistry and the compounds itself and targets and those types of characteristics. And if we had tools perhaps, such as quantum computers, but other tools as well, AI and things like that, that could accelerate that even by a year or two, and I think that's going to be possible, then we could really impact the world in a positive way. So that's one grand challenge area you. Is development of pharmaceuticals and medicine. You also think about, you know, reformulation of products, right? Where you have a lot of products on the market today, they could be laundry detergents, they could be dishwashing liquid, which may have, you know, ingredients in them that when poured down the drain, ultimately end up in our water system and ultimately don't have a positive effect on that water system that we drink that we use. And so could we reformulate some of that chemistry with better formulation? When we think about our energy systems, you know, the fact that we don't have batteries today in most of the energy grid, could we have breakthroughs in energy battery storage using these kinds of tools? So that's, I just find that eternally fascinating. And, you know, Vannegar Bush, right after World War II, Chris wrote a report which led to the founding really of nsf nih, so many institutions that have invested in fundamental research in the United States. And that report that Vannevar Bush wrote is called Science the Endless Frontier. And in that he put out a vision of the research university of a university that has a dual mission of teaching, but also advancing knowledge. He put out a vision of the government investing in early stage, basic fundamental research that might only have a payoff 10 or 20 years after that. And one of the reasons why we have the Internet today is because of Vannevar Bush's report, because the institutions he helped set up invested in that early, early few nodes of what today we call the Internet. And, you know, it's obviously the impact of that is, you know, vast and very wide. So. So I think that the spirit of curiosity, in terms of when you look at my activities, be it in quantum or be it in AI or be it in pursuing different, you know, X prizes and things like that, and starting with neuroscience, you know, the brain, I mean, what is more fascinating? The brain, of course, and how the brain works. This is still one of the key mysteries of our time. We'll look back 100 years from now and say, wow, the people in 2020 still didn't know how the brain really just like we look back to people two, 300 years ago and say, whoa, they didn't know about, you know, this science and that science. And the electron was only named in 1899. The photon was only named in 1920. And so we look back at people in 1850 and say, wow, they were missing a lot of knowledge. And so, you know, the brain and how the brain works is this absolutely fascinating area which I spent, you know, quite a number of years on as well.

Right, well, and maybe there's some overlap too, with Google, because I fairly recently read that they had a breakthrough with their quantum computer and that it generated around a million random strings of a number in about three minutes, which was like, amazingly light years faster, so to speak, than classical machines. You noted in your book that classical computing is quite different than quantum computing, both in types, the type of data and the type of processor. For someone like me, more of a layperson, non quantum, you know, literate kind of person, can you talk about those differences and what those, what difference those differences make?

Yeah, no, it's a great question about classical versus quantum computing. And, you know, fundamentally, if you were to build a building out of, you know, very traditional bricks, you can build one kind of building. But if you built a building out of super advanced carbon fiber, you know, all kinds of advanced materials, obviously you might be able to build a building that might be 300 stories high one day or some other grand Achievement. And that's similar here, where the fundamental building block, the Lego block of classical computing, by classical Chris, let's just mean non quantum. So any laptop that people have that are listening today, any supercomputer, such as the Summit Supercomputer in Oak Ridge, any mobile phone, is a computing device, right? So we'll call all that classical computers. And the fundamental Lego block there is the bit, right? Is the 110 on off bit that we have billions of in our various computing devices. And that bit, as we all know, is limited to just that, those two possible states, one or zero. But you now contrast that with the state of affairs in quantum computing, with the fundamental LEGO block is not the bit, but the quantum bit. And we shorten that to qubit, Q, U, B, I, T, or qubit. And the qubit is really different than the bit. In that, yes, it can be put in a state of 1, it could be put in a state of 0. So in that, in that sense, it encompasses the same two values that the bit has. But, but here's the, here's the key, you know, insight about quantum it also can take on any combination and to be more specific and technical, any linear combination. And for those people interested in that particular technical term, we can go online and check it out. But basically any linear combination of 0, 1. So you can imagine, to speak loosely about it, If I had 80% contribution of 0 and 20% of 1 or 30% of 1 and 70% of to 0, so any combination, because there's an infinite number of such combinations, because I can use any number as long as that adds up to 100%, then there's an infinite number of possible values that that one qubit can take. So, so in fact, just 55, 60 qubits, you're already at a point where even the fastest classical computer can hardly keep up with you.

Wow.

And because that classic computer is limited in that just one and zero. So it'd be like, you know, if I said, well, you know, could I take lots and lots and lots of bicycles and could I somehow match the speed of an airplane? Well, you can even take 100, 200, 300, 1000 bicycles, but it's still not going to get close to an airplane, right? And because the airplane is just so different in terms of its mode of travel than a bicycle, so that's kind of the leap that we have from a bit to a qubit. It's, it's a major difference. And because we're building these quantum computers that are qubits and not bits. We have just a vast computational space to deal with instead of this very limiting one and zero that we have in the classical. Now, it doesn't mean, Chris, that we're not going to have classical computers going forward. In fact, most of the programming, even when you're programming a quantum computer, it runs on a classical computer for most of the time. But then it. It basically sends messages and instructions from the classic computer over to the quantum computer where a calculation is done and then that message is sent back to the classical computer. So, so basically what the future we're thinking about right now that I put forward in the book is a hybridized future, a future where we have both classical and quantum computers sitting side by side, probably in a cloud. I wouldn't recommend, Chris and any of your listeners go out and buy a quantum computer. I think that it's easier just to use it on the cloud. There's lots and lots of cloud companies out there now that have announced that they'll have access to quantum computers. And that's one thing that's nice about this new technology. It's cloud native. It's going out on the cloud. And that means that students who are listening in today can just go online and for free, open accounts on various quantum computers and just start experimenting with this. And that's an exciting thing that this very expensive, very advanced technology that has just broken ground really in a big way, is so accessible to literally millions and millions of people.

It's a far cry from a stack of cards and dec 10 writers and things that I grew back, grew up with in the dinosaur era and Fortran. So now we've talked about quantum computing, but there's also, you've spoken about quantum information sciences and things beyond. Just thinking about the quantum specific to computing and quantum sensing and quantum communication. Could you unpack all that for us, too? What are your thoughts around those other kinds of things beyond just the computing?

That's a great point, Chris. You know, I think a lot of the press, if you read online or in print in magazines, a lot of it has been focused on quantum computing. And with good, you know, justification. It's a very exciting area. But I think that whatever reason, there's an inefficiency in the marketplace of information right now where people have not focused as much on these two other very strong pillars of quantum information sciences. And they are, as you correctly noted, quantum sensing and quantum communication. In fact, the irony of that, Chris, is that those are more near term than quantum computing. So there are things we can do with these very early stage quantum computers today. And I want to emphasize that to the, to the listening audience here, that the quantum computers of today are very early, very primitive. It's early days. Quantum computing, it's still very exciting and there's already some things we can do with these current devices. But quantum sensing, you don't need a million or 2 million or 3 million of these qubits that we talked about. You basically have the technology today. And so there's a startup that I have no connection with called Q spin. Qu. Spin. One word that has created a magnetometer. And what is that? That's a small ballpoint pen size device, literally the size of a pen that can detect very small perturbations and magnetic fields. And, and why would you care about that? Would you care about that? Because guess what organ of the body, as an example, has a magnetic field associated with it? That is a very important organ of the body, and that is the brain. Because if, you know, today, if you wanted to image the brain, if you wanted to know the activity of the brain, not just the structure of the brain, if you want the structure of the brain, you can take an mri. You can look at the brain, you can look at gray matter versus white matter, you can look at, you know, all kinds of structural issues. But the real analysis of the brain is not how it looks, it's really how it functions. And so the era of functional medical imaging, and specifically functional neuroimaging, is, is something that has yielded so much insight and so many insights, both for core neuroscience as well as for neurology. And so you have a modality called functional mri, SMRI for short. Really cool technology. But it's limited in temporal resolution and the resolution of time, because as fast as it goes, it's nowhere near as fast as the brain is working. On the other hand, you have EEG electroencephalography, which is temporally very fast. It's moving basically as fast as the brain because it's picking up those electric signals of the, of the neural spiking. The downside is one, you're really limited just to the cortical activity. And for the listeners who, you know, may not be familiar with all the parts of the brain, you know, we're talking about the external outermost layer of the brain, and that's really where you picked up EEG signals. And it's very difficult with EEG to triangulate deeper into the brain. But we know, of course, from brain studies that it's very important to look at how the Whole brain is interacting. On the other hand, if you could do meg, which is magnetoencephalography. So we talked about eeg, the electric signal, but every time, we know from Faraday that every time you have an electric signal, you have a magnetic signal. And so this magnetometer, this magnetic sensor developed by QSpin, could be used as a basis for an MEG brain scanner. And in fact, students, grad students at the University of Nottingham, if people want to look it up, built such a device for like under 10,000 bucks where they use the Q spin magnetometers to build a portable.

Wow.

MEG brain scanner. And so this is not 20 years in the future, Chris, this is now. I was in the UK looking at this device myself. Wow. So. So this exists. I touched it, it's there. And yes, it's early stage, it's a prototype, it needs some development work, but that's here now. And it doesn't need 2, 3 million qubits to make it work. So. So I think back to your key question here. I think that quantum sensing and then also quantum communication, the ability to communicate with, you know, these very interesting quantum states of superposition and entanglement. This, these are two very interesting areas that have not gotten the press that quantum computing has received. And I think that part of what I've been doing on this roadshow, on this book tour is to highlight these two other areas and to really share with the audience that there's really a seamless hole here. There's really a holistic view of quantum information science, as we call it, qis, which is all about the synergy across quantum sensing, quantum communications and of course quantum computing. Wow.

I'm going to talk about X prizes in Peter Diamandis stuff too. But it gives me great hope, you know, to the more positive kinds of things with, with the future and what the future can hold and some of the scalability of some things that just sort of seem like sci fi or magic at this point. So I want to. This is, this is super helpful and thank you for, for clarifying that in a way that's that, that I find very understandable. I want to circle to a presentation you gave at Taikon last year. You spoke about the non overfitting puzzle and deep learning and you've actually published a number of articles in this area. I was in Cornell's archive and saw those and I think just another one popped up in the last two, three weeks or something. Can you.

Co authored, Co authored.

Okay, fair enough, fair enough.

Yeah. I shared the responsibility with others so

could you discuss this further? Is that the same, the overfitting the same as the black box problem in AI or what. What again, for, for a.

No, it's a great question.

Lay audience.

Yes, that's a great question, Chris, to jump into machine learning here a bit. And again, machine learning is another of the most powerful tools of our time and we have yet to even see the, some of the, I think big developments that will come in the future with machine learning. I think we're far from the plateau of that technology. So as much as has happened the last five, six years, and it's been quite a ride, I think we're about to see more stuff. But one of the things that's holding us back in machine learning is that one of the fundamental characteristics of neural networks, which is that they have the ability to generalize and let's just define that a bit here. When we say generalize, what we're talking about as a neural network in the context of we call supervised learning. And so this is a style of training a neural network that says, hey, If I've got 100,000, 200,000, a million, 2 million examples of something, and they're all labeled, and let's say, for example, they're cars and buses and they're labeled car and bus and car and bus, and each one slightly different, different models of cars, different models of buses. But some humans have labeled them and that's the ground truth that we're going to use because several humans, maybe three humans each picture have labeled it. We took the top vote of all those ratings. And so we have a really strong ground truth data set. And for example, imagenet is such a data set that Fei Fei Li and others worked on. And MNIST is an example of a data set that a number of key people worked on. And so you look at these core ground true data sets and you train a neural network on that data set, you can then show it images that are not from the data set. And it'll do really, really well in categorizing that as a car versus a bus as an example here. And so that we call generalizing, we call it that it did not overfit. Overfitting means that if we show it lots of specific cars and buses, all it could do is tell the difference in those specific cars and buses that we showed it in the pictures, but it couldn't generalize to any car or bus that's in another picture that it wasn't shown. And so this we know, here's the good news Chris we know that it can, it can work. We know that it does generalize when we train it in this way. But the question is, how does it do it? And that's what's still a mystery. That's what's still a mystery. Now I'll tell you another mystery is how does the brain do it? Because the technique that we use in neural networking, the mathematical technique, we call it gradient descent as part of what's called back propagation. And this is something that Jeff Hinton and others worked on back going back to the 80s, 1986 to be specific, in a, in a, in a collection of articles and papers called PDP Parallel Distributed Processing. And that was the rebirth of connectionist architectures. Architectures, we call them connectionist if they're based on inspiration from the brain, which is a connectionist architecture, a connection of a web of many, many nodes coming together. And so the interesting thing is that while it works really well in neural nets, when we look at the brain, we actually don't see this kind of back propagation that we're using in our computers. So there's, there's several mysteries here. Mystery one is how does generalization work? And that's this series of papers that are led by my colleagues at MIT that I participated in. And then you have another conundrum which is the brain seems to have gone another way in terms of how it updates its weights. So we know of course, that the synaptic strength, the synaptic connection between neuron to neuron does change and modify based on our activity, based on learning a language, based on learning a new skill, based on learning how to ski. All these different experiences would change the synaptic connections and waiting in your brain. But we're still not sure exactly how that works. We're not sure of the complete algorithm that the brain uses to make that happen. The papers that you're referencing and people, as you pointed out, can find them for free online on the Archives and arXiv.org which is a wonderful site started by my, my friend and colleague Paul Ginsburg, who just had the insight and inspiration to start a site that shared all the scientific knowledge for free. But yeah, people can find these papers online on the archive and, and, and join in, please. I, I invite people to join in on this investigation of how to neural networks go from training on one data set to then be able to analyze and correctly classify a related data set, but not the same data set. So it's absolutely fascinating question.

We'll put some links in the show, notes for all that too, for people to be able to take a look at it. It is a fun, you know, for me, easily intimidated kind of space to be, you know, wandering around in, but educational nevertheless, less so. You, you've also discussed bias in AI and I had a guest last year, fascinating technologist artist Heather Dewey Hegboard. And she talked about how back in the day, looking at facial recognition, which, you know, everyone, you know, thinks about now and from Facebook to, you know, interesting movies and stuff, and that the algorithms were predicated on like whatever prolific images were at the time that they were doing that. And the inputs that when this was being done was being overrepresented by George Bush of all people who was president at the time of their development. So I'm just wondering like other, what other biases exist in AI and how do they become incorporated and you know, how do you find them, how do you, how do you fix them?

Is a great question. I was just speaking today about this very issue. And so to me, the first and foremost responsibility we have is to look at the data set that we're going to use to train the machine learning model. Because very often that bias is hidden deeply in, within that data set. And there's several recent cases that bring this to light. So a large bank in the US is being sued right now because they use the data set to train a machine learning model to give out mortgages and set mortgage rates. But the model is built and predicated on the data set that had bias in it in terms of different groups of people were given mortgages, others were denied, others were given better rates, worse rates. And so by building a machine learning model on that historic data, that was a major problem. And that bank should be, should have really known better. And unfortunately they went ahead and trained it on that data set. This is the data that they had. But that data was based on human decisions which were biased. And so it wasn't so much that the machine learning model started off and introduced the bias. The bias started with the people.

Yeah, right.

That's where the bias started with human beings. And unfortunately that bank carried over that bias into the machine learning model. So we've got to get very aware of that. If we talk about the healthcare content, then we have to also be aware of heterogeneity. And what I mean by that is that if we're going to create a data set of medical images, let's get, let's call it cats, and we're going to focus on the lung and we're going to determine whether there is or is not a tumor in that lung. And the ground truth will be a series of CAT scans that radiologists have read and stated their diagnosis of yes or no, positive or negative. Well, in that case, we better make sure that, that we have a variety and diversity of data, and that includes making sure we're using multiple output of medical images from multiple scanners to remove and wash out any artifacts in any particular scanner. It'd be very helpful to do that from across different hospital systems to make sure we don't have any bias in terms of a particular way that certain radiologists in a particular hospital system are grading their tumors. We also want to make sure that we're demographically diverse because again, there may be demographic dependencies in terms of different groups and how they exhibit that, you know, their particular tumors. And so we've got to be really, really cognizant of these issues and just saying, oh, I found a data set of 20,000 CAT scans and I'm going to build a whole machine learning model that I'm going to roll out to the world. That's a very, very potentially dangerous prospect.

Yeah, that I think you raise a good point.

So we want to be very, very cognizant of this, very aware of it, very, you know, mindful. And so, you know, there's several tools out there that people can start to use to probe their data set and see if there's any issue with their data in terms of having any skew or bias in that data that they should be aware of. And so we've got to really focus on this as a key, as a key issue.

Yeah, I've seen a lot of it in the healthcare things that you've been talking about. And the issue, I think also sometimes people get to a point of saying, okay, I can't understand this stuff. There's people smarter than me that do this. I just want the easy button. So, you know, I press the easy button, I get an answer. I presume that that answer is right because, you know, someone else has done everything that they're supposed to do with it. And it's just like back to your bank example, you know, if to follow the trail back to the initial things of how, you know, what predication's based on, then, you know, it can make all the difference and get, I think, further muddled. You know, over the course of time, I've, I've been talking with the group, a potential startup with, looking at, trying to take some of the complexities of, let's say, Someone's blood panel, someone's microbiome data, someone's DNA genetic data, and then trying to weight it and then to take a look at making predictive analyses as to early detection of certain kinds of disease states, in this instance in particular in behavioral health and man, it takes about 10 minutes in a conversation to just go, you know, just to throw your hands in the air and go, this is impossible. But, you know, with the conversation about you around, the whole quantum aspects of this, I mean, it gives me sort of a better hope that those kinds of things will, you know, will be able to be happening and won't be too far down the horizon. So do you have an encompassing vision for quantum computing? You, you have a very far vision in general, Jack. I want to get into that. I want to talk about 10x thinking and 100x thinking and stuff like that in just a second. But where do you, if you get out your crystal ball and make a prediction and you get, I think probably also to see a lot of the inside of startups and promising technologies getting ready to scale and get out there. But what's your near term or midterm vision for where quantum computing or quantum information science is, is headed?

Well, yeah, I think it's, it's really. We're very lucky to be alive right now, I think at a time when, you know, the initial vision for quantum was set forth in 1979 and 1980. And in my research in this area, what I discovered is that, well, Richard Feynman is often absolutely quoted correctly in his statement of nature is not classical and therefore we should have quantum mechanical computing devices. Actually, there's a paper before Feynman by Paul Benioff. And Paul Benioff wrote a paper in 1979, a year before Feynman's speech. And that paper spelled out really all the elements of a full universal, what we call Turing complete quantum computer. And so I want to give credit on this podcast here to Paul Benioff. He's still alive and still active in computer science and somebody who really had a very early vision of how this was going to the possibility of building such a device. But often he gets overlooked in the histories of this area. So in the book, in the second chapter, where I go into the history of the field, I really wanted to home in on exactly how this thing all came about. And that's when I uncovered Paul's papers and very early insight into this area. So I do want to give credit to Paul there. But, but I think when we look forward right now, you know, so basically about 40 years ago, exactly this year is when, you know, this concept of quantum computing really started to kick kick up some dust. And by the way, a side question, Chris, that we, I don't think have time to go into in this podcast, but just to leave the audience with a teaser that they can contemplate and ponder. Why did it take 50 years from 1930 to 1980 to really conceive of the idea that you can use quantum Mechanics for computation? 1930, Chris, there was already a very well understood, understood and well formulated framework of quantum mechanics. We already understood that the world was very different than the classical world that Newton had put forward. And so it was already well understood the principles of superposition, of entanglement. One time it would come a little later, 1935, let's say. But superstition, certainly uncertainty, the principle of uncertainty with Heisenberg matrix formulation of quantum mechanics, Schrodinger's equation, Dirac's equation was 1928. And. And so all this happened by 1930. And people along the way, like John von Neumann, who flourished in the 30s, 40s and 50s, was somebody who was had one foot deeply in quantum mechanics and one foot deeply in computation. And so it's a fascinating question as to why did it take until 1979, 1980, for people to realize we can use this? So, you know, that that's maybe the subject of another podcast one day, Chris, in the, in the history of ideas and development ideas. But it's fascinating. It raises the question for us today, what are we sitting on right now that we should be thinking about? But coming back to your question about the future of quantum computing and the vision going forward, I think it's a fascinating time because in the last few years the field has been able to actually build these devices in a way that it just didn't have the ability to say 10, 20 years ago. And now that we have several different. And one thing I think is fascinating and exciting about this field now is that there's basically eight ways to physically build a quantum computer or variations of these eight ways. There's some nuances there, but. But what's cool is that we're not having group think and saying, oh, let's all just focus on one way of building it and put everything behind that. Yeah, what I love about the field right now is the thought diversity, the fact that we can have diversity of thinking and opinion. And some groups, labs and companies are pursuing approach number three, some are approaching, some are using approach number five. And so each of these approaches be a trapped ion neutral atom, photonic superconducting qubits. I mean all these different approaches of building a quantum computer which are really quite different. I mean it's completely different physics. Of course, they're all pursuing them with relentless effort, you know, in going after them in their own way. And so that's one thing that really gives me a lot of confidence and optimism that we are going to have a scaled quantum computer in a time period that we all, in our working lifetimes can actually use it and start to have impact with it in medicine and energy and many other sectors. So that's on one, on the, on the physical aspect. I'm also quite excited about the algorithmic aspect, the software aspect of quantum computing. And I think that I'm seeing a lot of great development where Alon Oscar Guzak, for example, a researcher who's now at Toronto, a leader in the thinking around quantum chemistry, is somebody who's doing incredible work in that area and ultimately will lead to great breakthroughs in the use of quantum computers for breakthrough chemistry from new materials. So, so there's a lot of great reason to be hopeful about the use of quantum computers in terms of the software that's going to run on it, the algorithm that will run up, because some really smart people are working on that now as well. I also think that, you know, quantum sensing and quantum communications we discussed are beginning to flourish. Again, people have been ignoring those compared to quantum computing. But you know, just in the last six, seven months I'm seeing an uptick in people's interest in those two areas. And we're seeing companies like QSpin, the one I mentioned before. I'll mention another company here which is AO Sense. I have no interest in that, no, no financial interest in that company either. But I do want to mention AO Sense because it's a company that's using quantum sensing for another application. Not for the magnet magnetic application I mentioned before with meg, but in this case with navigation. Imagine you're in a car, either you're driving it or it's a self driving car. Either way or imagine you are in any kind of other vehicle trying to navigate and you get cut off from gps. Whatever reason, you lose the signal, you're in the middle of nowhere. Or there's even many highways that you know. Highway 280 for example in California. The Bay Area is well known and notorious, which is the irony of course, the middle of Silicon Valley as not having coverage in key area. So let's say you're in one of these areas and you absolutely need to have good navig navigation. Well, you can use a quantum device to provide very precise navigation with no gps, no access.

Wow.

So, so these are really fascinating. People can look up ao, sense the letters AO and then sense and you may want to provide links.

Yeah, we'll get that in there too.

So, so, so these are companies. There's a startup spinning out of great universities and smart people and we're just seeing a lot of diverse ideas out there when it comes to this space. You also have companies that are popping up like Hugh Control out of Sydney, Australia. Mike Bier said you have companies such as Quantum Machine, think that coming out of Israel. These are tools based companies, control companies that are helping the people who build in quantum computers have better control over their cubits. And so they're like the picks and shovels providers, right? We call picks and shovels providers. Those companies who will be like, you know, in the old days in the Yukon, you know, they're not going to dig for the gold, but they're going to sell you the pick and shovel for the gold. Right?

Yeah.

And, and so these are picks and shovels companies that are helping so many different organizations control their qubits and get more precision out of them and get higher fidelity out of them. And so, you know, that's an exciting prospect as well. So I think all this is really conspiring in a good way to bring about, you know, the kind of computing and sensing communication devices that are really going to have big impact. And again, with sensing communication, we don't have to wait years and years and years. We can start having that impact right now.

That's. That is very optimistic and very exciting. I'm tickled. And will continue to keep a better eye out in this area. So thank you for this primer on quantum everything, Jack. That's terrific. I want to be mindful of your time. I can't imagine that you're not jet lagged, but I do have a few other questions I'd like to ask if you have the bandwidth for it, that I would just feel guilty that I didn't be able to share about you with our audience. Do you have a few more minutes you can spare with us?

Yeah, let's just do maybe one or two more because it is 1:45am

all right then I will edit this. We'll have to do a round two. You are big into moonshots and mutual friend Peter Diamandis and his starting of the X prize and all things sort of X and Google X. Can you maybe give us a, an overview of what that work is like and what, what you're doing with that?

I'll just speak here on this podcast about X Prize because that's a nonprofit I serve on the board of, and proud to do so. And that's the one that was started by Peter Diamantes, as you mentioned. And xprize.org is where people can find that website and participate in any number of ways in this nonprofit activity. And it's exciting because moonshotting is the kind of activity that anybody can do. You can be a student, you could be retired, you could be in engineer right now, you could be a doctor. So whatever role you're playing in society, you could be doing moonshotting and you could do moonshotting in your own company, or you could be doing moonshotting with others as part of formal moonshot teams at XPRIZE or other places like that. So it's an exciting moment when moonshotting is really taking hold and lots and lots of people are co opting this word. And I'm happy about that. I'm happy that X Prize was able, along with others, to popularize this term and get it out there in a big way. And I think moonshining is the kind of thinking that says, you know, going to space without a government sponsor would have been considered absolutely ludicrous, you know, about 20, 30 years ago. And when Peter and others started the X Prize for space, it was considered absolutely ludicrous. The idea of building a vehicle on your own, not having a contract from the government and then launching and being in that vehicle yourself, going to space yourself up and back twice in two weeks, I should mention, was the criteria. Okay, I remember that is, yeah, that's a bold statement. That's a bold vision, a bold challenge. And sure enough, 21 teams competed. And you know, Bert with Hanstein was the first to go up there and back. And he did it. He did. Richard Branson then showed up short while later and decided to license that technology. And now that's, that's become Virgin Galactic, which recently has made public pronouncements that they're going to be going up sooner rather than later with paying customers. Wow.

So, wow.

You know, it's, it's exciting to see the evolution of that technology go from literally a conversation in a small room, you know, around X Prize to now a global conversation around how lots and lots of people will get access to space. And if you look at Elon Musk and SpaceX, you know, he, he credits, and he's been very involved on the board of X Prize and he credits X Prize for a lot of his thinking and breakthrough around saying that yeah, SpaceX can do many things that would have cost billions and billions of dollars before, but he can do it for maybe half that cost or even two thirds of that cost. So, so it's really exciting to see the inspiration and the ripples of X Prize in so many different people's minds as they compete for X Prizes or they start companies or they get on boards of companies. So it's really had a massive impact in my own life and many other people's lives already. And I think we're going to meet the grand challenges, Chris. We're going to meet the grand challenge of food for a planet of 9 billion people. 10 billion people. Coming up, we're going to meet the grand challenge of energy getting towards a clean energy future. We're going to need moonshot thinking.

Yeah.

And it's thinking that says, you know, we can go for some incremental improvement, but ultimately it may take these leapfrogs to get us where we want to go. And you know, you look in the past, you look at the green revolution that was catalyzed in India and then other spread to other countries and led to the feeding of billions of people with nutritious food products that otherwise would have probably perished, certainly malnourished. I mean that, that's just a great example, you know, of moonshot thinking before even moonshots were really kind of a big thing.

Right.

So, so this kind of, this kind of thinking really is impactful and I'm excited to be part of that.

That's fantastic. I really appreciate it. And I, there's some talk, I think I'd read of your, or seen of yours, where you were talking about the difference between that, the incremental thinking in the 10x or 100x. And I've just, you know, again, armchair tried to practice that on some issues and problems and challenges that I've been having lately with my work. And I, I have to say it is a very helpful kind of thought experiment to kind of, you know, very freeing and, and I think it really helps come up with better creative solutions and, and even working together in teams of better brainstorming together with other colleagues. And it's just, it's a, it's a wonderful model. So we'll put some links in for that and the grand challenges as well. So, so my friend, I have probably about three more days worth of questions and things I'd like to talk about with you. But again, you have been so generous.

Well, we'll set up another time.

That would be great. We'll do a round two. So thank you.

And we'll do a round two. Well, Chris, yeah. Great to be with you today.

Great to be with you. Awesome.

Great to be with the listeners and you know, look forward to comments and input from the wider audience as people start to hear about these cutting edge technologies.

That is great. Well, let me put in one last thing that your book is available via Spring Springer or Amazon or wherever people consume their books. The articles that you're co author on are freely available@archive.org I'll put that in the show notes because it's a little tricky spelling and if listeners want to learn more about you or your work or get involved or invest what are some of the best ways to follow you or reach out and learn more?

Sure. The two websites I'd recommend are the GitHub site for the book. So people click on the link that you'll provide to the GitHub site, which is. And people can also just search for it under Quantum Computing, Book Hidary and GitHub if they just search for that. Or my foundation website, htoryfoundation.org you can also get to me from there, but my email addresses are on both of those areas and so people can reach out and happy to engage.

Awesome, Jack. Well again, thanks very much. Hit the hey, good luck with your presentation and conference and looking forward to what comes next for you.

Great. Thanks Chris. Great to be with you. Have a great night.

Living a Life in Full is a production of Stout Media, a subsidiary of Gordian Knot, LLC. Assistant producer Gracie Wong Music Dan O' Brien executive producer and host Dr. Chris Stout to learn more, stop by our website at lifeinfull for show notes and please recommend us to your friends and subscribe on your favorite platform. And as for the obligatory disclaimer, this podcast is for general information uses only and does not constitute the practice of psychology, medicine, nutrition or other professional healthcare services, including including the giving of medical or psychological advice. No doctor patient relationship is formed. Lastly, and perhaps most importantly, I take conflicts of interest seriously. For all of my disclosures as well as show notes, please see livingalifeinfull.org podcast and my LinkedIn profile. Thanks and until next time,