Manager, Derivatives and Alternatives Risk & Quantitative Investment Solutions Group, Western Asset Management
By David Zierler, Director of the Caltech Heritage Project
October 27, 2023
DAVID ZIERLER: This is David Zierler, Director of the Caltech Heritage Project. It is Friday, October 27, 2023. It is great to be here with Dr. Robert Michael Gingrich. Bob, it's so nice to be with you. Thank you so much for having me.
ROBERT GINGRICH: It's great to be here. Thank you.
ZIERLER: Thank you for having me in your house, by the way. This is my second time here, I'm so happy.
GINGRICH: Yeah, yeah.
ZIERLER: Great things happen with chance encounters. You start talking, and connections are made just like that.
GINGRICH: Yeah, the power of networking. It definitely works.
ZIERLER: To start, would you please tell me your titles and affiliation?
GINGRICH: Sure. I am the Manager of the Derivatives and Alternatives Risk as well as the Quantitative Investment Solutions Group at Western Asset Management.
ZIERLER: Let's translate all of that. Derivatives and Alternatives Risk. What does that mean?
GINGRICH: Derivatives, within finance and quantitative finance, are something that derive their value from some other security. Usually, there are a number of different mathematical models that describe that relationship, and there are a lot of ex-physicists and mathematicians that kind of run those models. For derivatives at Western Asset, I'm the risk manager of those, I make many of the decisions about how we're going to model them, how we're going to decide on the risk. Alternatives are alternative investment strategies, which is kind of a broad and vague term, but in my case, it's more of our hedge fund-like instruments, or our more unconstrained portfolios that can kind of go into a bunch of different markets. Generally, they're generalist kinds of portfolios, so they may invest in US rates, global rates, currencies across the world, credit spreads, mortgages, a variety of different areas, wherever they see fit.
ZIERLER: Who are the clients of the company? Who do you work with?
GINGRICH: We manage money for both retail and institutional clients. On the institutional side we are talking about pension funds, insurance companies, sovereign wealth funds, corporations, things like that.
ZIERLER: This is mission control with all of your screens up right now. What do you do on a daily basis? What are all of the graphs? What are the things you're following?
GINGRICH: I'm doing analysis for the investment desk, understanding the risk of our portfolios, interpreting all the numbers that come out of our risk system, and also meeting and talking to clients. Clients have questions about risk. They want to talk to someone who goes through the numbers. Those would be my primary responsibilities, especially on the risk side. Also, part of my responsibilities is in this QIS or Quantitative Investment Solutions group, which is looking at big data,machine-learning applications and quantitative portfolios. Some of those would be internal applications, some of those are direct-investment applications.
ZIERLER: Everyone is talking about artificial intelligence and machine learning these days. What are you seeing? What are some real-world impacts? Where is the hype, and where is this really starting to make an impact now?
GINGRICH: Sure. My group's been around for, gosh, about five years now, so well before ChatGPT. During that time, we looked at a lot of what I would call more traditional machine learning, which you could think of as an extension of ordinary least squares analysis, which has been around forever, hundreds of years, principle component analysis. But stretching it out to work on large datasets, using nonlinear models instead of the traditional linear models. We had looked at some of that for natural-language processing, and we also looked at some of it for analyzing market data and making predictions. For instance, in the mortgage pool space and also in ESG, looking at the effectiveness of ESG, which is environmental, social, and governance factors, to see how those affect forward-looking returns and see the relationship, which is still a point of debate among many people.
ZIERLER: How long have you been with Western Asset Management?
GINGRICH: About 12 and a half years now.
ZIERLER: Were you part of the founding of the company, or it goes back farther than that?
GINGRICH: Western asset was founded in 1971, so well before I started.
ZIERLER: What you're doing now, is that more or less what you've been doing throughout your 12 years with the company, or have you switched roles over those 12 years?
GINGRICH: I've changed roles a bit. When I first started, it was mostly the derivatives modeling. We were building an in-house risk model, just a big process that runs every night and calculates the risk for all of the portfolios. We were building an internal one, so that was mainly what I was hired to do, the derivatives modeling, and help build that out. That's mostly built. And then, five years ago was the QIS group, which was the machine learning and the big-data applications. Also, throughout that time, we had a strategy that grew fairly large, an alternatives strategy. The risk management of that particular strategy took a lot of my time, a lot of client meetings, a lot of extra analysis.
ZIERLER: Before COVID, would you travel a lot? Or this was always an office-based situation?
GINGRICH: It's mostly office-based. I would go to a couple conferences a year, probably two or three. It's been a little less since COVID, just because there have been other things going on. But maybe a couple conferences a year. I went on a three-week tour around China and Asia in general that was meeting with clients mostly, also combined with a conference. I rarely fly out to see clients. Usually, I see them when they come into the Western Asset Office.
ZIERLER: Is Los Angeles or even Pasadena the hub for this kind of business model? Are there other similar companies that cluster here? Is most of the action in Silicon Valley, New York? Where are other kinds of companies operating in this space?
GINGRICH: As far as finance, especially quantitative finance, I would say New York is the capital. London is also very large. They would have a higher concentration of quant shops. However, Southern California does have a number of large fixed-income shops, which would summarize both Western Asset, where I am now, and Pimco, where I was previously. There's Pimco, there's TCW, there's DoubleLine, there's Payden & Rygel, there's Guggenheim. These are all pretty big fixed-income shops.
ZIERLER: Would you call yourself, what we call in the popular imagination, a quant, a math and physics whizz, and you apply those skills to finance?
GINGRICH: Yeah, that's what I started as at Pimco, and I still do that from time to time now, sort of the extension of responsibilities of a quant.
ZIERLER: For your career, and we'll get into it chronologically, but were you always on the business track but wanted to do the PhD for the skills in math, physics, and quantum information? Or were you on an academic track, and this was a pivot for you to finance?
GINGRICH: That's kind of an interesting question. I went into physics grad school pretty much just because I loved physics. I thought physics was great. My undergrad at UC Santa Cruz, I did a physics and math degree, and I felt like, "Oh, this is the subject that I like studying. I'm not weird, I'm a physicist," [Laugh] that's kind of what I figured out during those years. When I graduated from undergrad, I was thinking possibly, I guess, about something in Silicon Valley, but I really wanted to do more physics, so I went into physics grad school and ended up getting into Caltech. As far as the business side, being in physics, I wanted something that was exciting and that I thought might be applicable soon. And I liked computers as well. When I heard about quantum computing, which John Preskill was doing and was a growing field at that time, though much smaller than it is now, it just sounded like the coolest thing I could possibly do. Building a better computer using quantum mechanics. I studied that. But as far as going into business, it wasn't really even until my postdoc, I think, that I even learned what a bond was. I was helping my girlfriend at the time, who was getting her MBA, do one of her math classes. That's when I started learning what a bond was, what kind of analysis they do. Seeing the math they use, I thought, "Oh, this is actually kind of interesting. It's a nontrivial application of mathematics that's actually used in the real world." That got me interested in it, then I started studying a little more, and eventually switched over.
ZIERLER: Did you do any formal business education? Did you think about an MBA? Or is it just on-the-job training, you pick up what you need?
GINGRICH: When I was thinking about making the switch, I was doing a postdoc at JPL in quantum computing, but I started reading about derivatives modeling and that kind of stuff, so I bought some books, started reading about that, taught some of it to myself. Then, I found a postdoc with Mark Wise, a physics professor at Caltech.
ZIERLER: Yes, I remember that he became interested in finance at some point.
GINGRICH: At the time, and this was 19 years ago, he was working with Vineer Bhansali at Pimco and was interested in finance and financial modeling. I believe I may have reached out to John Preskill first, but he suggested I talked to Mark, so I did, and we arranged this postdoc. I did a brief postdoc technically in the physics department but doing finance at Caltech. That lasted about nine months. During that time, I sat in on a number of classes and read some more books.
ZIERLER: Did this bring you into HHS at all? Did you do any econ in that division?
GINGRICH: Yeah, I sat in on classes. As a postdoc, there was no need to sign up. I would just go to the class and then do the homework. I don't remember the names of the courses, but there was a derivatives-modeling and sort of an equities-modeling course, one or two others I took that I imagine would've been within the HHS department.
ZIERLER: I want to ask some overall questions about quantum information, both as fundamental research and all of the exciting ways it's being applied and even translated right now. Are you still connected at all? Do you stay on top of the literature, the really fundamental stuff? Are you excited about the next paper on arXiv about error correction, those kinds of things? Or that's sort of too removed from what you're doing now?
GINGRICH: I keep up with it. Really going through a very technical paper is pretty time-consuming, so it's usually more I read the abstract, I maybe read the first couple paragraphs, I kind of page through it a little bit. I'm doing due diligence for a small quantum computing and quantum technologies venture capital firm. Through that, the general partner, Nardo Manaloto, is going out and talking to companies all the time. He'll find an interesting company and send me their pitch deck. That'll usually point me to a couple of papers, so I'll read those papers to what depth I can. If it's a very esoteric, experimental one, there's only so much I'll get out of that. But often, they have either written a paper with someone that I know, or I might be able to lean on some of my old connections to get some impression of their reputation and work. Or just read the paper and sort of understand what the innovation of that company is. I've looked at probably 40, 50 different companies now, so that gets me reading papers from time to time and keeping up with the field.
ZIERLER: Here's what I've been looking forward to asking you since I met you, given your dual expertise in quantum information and now in business. From the Microsofts, Googles, Honeywells, Amazons, the major corporations are pouring billions and billions of dollars into quantum information research in order to build a scalable quantum computer. Startups are doing the same. There's a level of investment in what must be considered fundamental research because nobody's really sure what a quantum computer will be good for, so it's still really fundamental. We can't think of it fully as applied. We haven't really seen this since the heyday of Bell Labs 60 or 70 years ago, and there, of course, there was a monopoly that funded all of this. This is all coming from these companies themselves. Why? Why this level of investment if the end product is something that is so poorly formulated in terms of exactly what a quantum computer is, exactly what a quantum computer will be good for? These are still open questions. From your perspective, both in the quantum information research side and the business side, what explains this trend over the last 20 years?
GINGRICH: Well, there has been technological progress in the area. And the last two years might be a bit of an exception, but there has been a willingness to invest in ambitious technology projects.
ZIERLER: We've seen a dip in the last few years?
GINGRICH: Past couple years, yeah. Many of the companies you're thinking of, like the public companies in the quantum computing space, they've seen, as with many technology companies, a notable drop in their stock prices. If you go back to the height, back in 2021, I would say yeah, there was probably too much enthusiasm. There was a lot of short-term enthusiasm in the field. I think the short-term enthusiasm is certainly bubbly. There are some areas where people are just too optimistic. But the long-term enthusiasm is well-justified. I think the best way to think of it is, this field is pushing the boundaries of precision measurement and precision control. And we've been doing that, as human beings, for centuries, and that has gotten us great things. It's gotten us the lightbulb, modern computers that we have today.
Over long periods of time, much of scientific advancement has been about more and more precise measurements and more and more precise control. We're pushing the physical limits of that now, and if we can get to the physical limits, we've shown that we can do these cool calculations on a quantum computer, which would have some useful applications. But I do think there are a lot of other applications along the way that we should consider as well. Focusing only on quantum computing is kind of hoping for the home run. There are a lot of other areas of quantum technologies that would be more like singles and doubles that could be highly transformative and lead eventually to practical quantum computers.
ZIERLER: Even if the big push is to make a scalable quantum computer, what you're saying is, in the course of this research to get there, there have been all of these branches from this main trunk, if you will, that have gone on to already show demonstrable success?
GINGRICH: There have been some, so yes, but also, there are some promising ones that may be closer to reality than a full-fledged error-correcting quantum computer. Things like magnetometers that are much more precise, I think quantum random number generation is a very interesting field, where we're already using quantum mechanics for the very important application of better encryption. There's also getting past the wavelength limits of measurements. I just saw an article about LIGO getting past that limit. You're using quantum phenomena to be able to do that. And you are, indeed, getting more precise measurements and more precise control. Who knows what the applications will end up being useful for? GPS would not be possible without very precise clocks, very precise measurements of distance and time. And GPS is hugely useful.
ZIERLER: Microsoft and Amazon, for example, are pursuing quantum information, building the quantum computer, in very different ways. Are you following those distinctions? Does one seem more exciting or plausible to you than the other?
GINGRICH: I follow them. I would not say to the level of a researcher in the area. I follow what they're working on. I think any application for quantum computing is still at least five years away. And I kind of have a philosophy that if it's more than five years, that means we haven't gotten it working in the lab yet, and if we haven't gotten it working in the lab, we really don't know how long it's going to take. You're talking about science at that point as opposed to engineering.
ZIERLER: You're saying that there is something that's working in the lab now?
GINGRICH: Well, there are other things. Getting past the quantum limit for optical observations. That's something that works in the lab. I don't think it's practical to do at a doctor's office yet, but it's working in the lab. That's probably just a matter of engineering until it's hopefully useful. I'm just making the distinction that that's a little more predictable. If it works in the lab, it's going to work in the real world, or at least there's a pretty good assurance of that. But if it's not working in the lab, you really don't know how long it's going to take.
ZIERLER: It's remarkable how these enormous companies, the biggest companies out there, are doing this. But then, you have these startups that are on a shoestring budget, and they're competing in the space as well? What's the takeaway here, from even a business perspective? How can a company with such a limited resource base also be doing interesting and important work in quantum information?
GINGRICH: A lot of those smaller companies are coming out of academic research labs, so they've built up a lot of expertise, and they may have found their own unique solution to a problem that the bigger companies are looking at. I think many of them hope to eventually be bought by that bigger company. Especially something that is only based on a full-fledged quantum computer coming about, I think that is pretty tough for private funding, especially on a small scale, to go into. When we look at companies, I always want to find a company that can make money in the NISQ era. NISQ is Preskill's term for noisy intermediate-scale quantum. And I think that's a good way to describe these applications that will be useful before a full-fledged quantum computer is there. I always want to find a company that can make money in the NISQ era. If you can only make money if there's a full-fledged network of quantum computers, you probably shouldn't be taking private funding, especially small-scale private funding, at this point.
ZIERLER: What about federal support for quantum information? For example, you mentioned precision measurement. The things that are happening at NIST, for example. Are you following those developments, the way that federal dollars are supporting basic research, even in the private sector?
GINGRICH: I always encourage the companies I talk to to look for grants or non-diluted funding, as we call it, because there is a lot out there. I would say it's very promising and interesting, especially with the CHIPS Act. I've seen quantum computing explicitly mentioned, so there should be more federal money going into these kinds of programs. And that's great for private investment. The government will help you develop your product. But as far as detailed understanding of who's going to get what funding, I just usually encourage our companies to make sure they're applying for any grants they can get.
ZIERLER: What would be a good way of understanding your academic expertise in quantum information and how you're applying that in order to make good business decisions for your company? How do you translate one to the other?
GINGRICH: I would say I know more about finance and investing than physicists would. I remember when I was a physicist, I really didn't know anything about investing, but I've been doing it for 18 years now, so I'm familiar with watching the trends. I've sat next to well-known investors like Bill Gross and Ken Leech, I've worked with them. They're managing trillions of dollars, so you're exposed to a lot of information. I feel like I have more of an understanding of how the markets work than most physicists. With my PhD and additional studies, which I've been doing since leaving the field, I still probably know more about quantum computing than most venture capitalists would.
ZIERLER: How would you apply that to having a good BS detector? Because there's a lot of hype in quantum information. What would be an example of you saying, "This is for real, and this is not for real"?
GINGRICH: Some of the companies I've looked at for angel investing or VC, in my opinion, some were charlatans or even outright trying to cheat the system. There was one company we looked at that had some grand predictions about what they could do with quantum computing, and it was something neuroscience-related. I went into their data room and read their documents, and I swear they must've generated it with an AI because it was very jargon-heavy, but all used in a nonsensical way. The jargon, in a sense, was used correctly, but there was no overall point to the whole paper. It was very odd to read. Usually, when someone tries to write something with a lot of jargon, they use the jargon incorrectly, so they didn't quite do that, but they just never really said anything through the whole document. I even said that to our general partner of Qubits Ventures. I never heard anything back, and we never invested in their company.
ZIERLER: They were probably hoping a Caltech PhD would never read it. [Laugh]
GINGRICH: I don't know, but it was an odd experience to read the whole document and just think, "How did they manage to say nothing in that whole document?" But they didn't. And others, it's maybe someone independent doing research, so I'll read their description. If I can't at least get some reasonable understanding of what they're saying, that's pretty worrying, and my recommendation is definitely going to be to not invest in that company. That can help for being a sort of BS detector, as you say, and also having connection to the field. If I know the person used to work at Caltech, and I know people that worked with them, that also helps. I can sort of verify they have had a legitimate career in the space.
ZIERLER: When you get pitched, when a company comes to your company with a pitch, what are they looking for from you? Is it seed funding, is it to get to the next level? What does your company provide for these companies?
GINGRICH: For the VC fund, Qubits Ventures, its seed and pre-seed. And on that note, I'm also an active angel investor and have been for about eight years, and that's generally seed and pre-seed but smaller checks. We don't write million- or multimillion-dollar checks. Qubits Ventures checks are around, $100,000 or $500,000 to help the company get started, to give them some connections and help them grow to the next level.
ZIERLER: What are the batting averages in the VC world for you? What does success look like? What are those numbers? 1 out of 10? 5 out of 10?
GINGRICH: My angel investing experience is not unlike what people warn you to expect, which is that maybe 1 out of 10 will work to be a home run.
ZIERLER: And that funds the nine that don't, that's what makes it all make sense.
GINGRICH: Pretty much, yeah. You might get something on some of the others. You'll probably have a few that just die completely, either you get a small fraction of your money back or nothing. And I've certainly had several of those. And then, you'll have some that maybe grow a bit, but there's no real monetization event. You can wait five, seven, ten years. A couple of my companies, yeah, I've been waiting at least five years, and they're not really much bigger or smaller. They could still take off, but they may end up going away. But then, I had one company that did very well, and that kind of funded most of my angel investing. Then, I have some other companies that have grown but it's still on paper that they look decent. The last year or two, there's been kind of a drying-up of this private VC investing and tech investing. Kind of hit pause on a number of those.
ZIERLER: Are there firewalls for you to act as a consultant? If you see a company that's sort of in stasis at five years, if you have an idea to help them along, are you able to share that idea? Is that a conflict of interest?
GINGRICH: I get pre-approval from Western Asset for private investments to prevent conflicts of interest with them. That is my main job and what pays my salary. That is the bulk of my work. For instance, being on the board of a company, I can't really do that, that would be a conflict of interest and wouldn't really work. However, personal investing in private investments, especially if they're not directly in the finance space and wouldn't be thought of as a competitor to Western Asset, that's all fine. And even the venture partner aspect with Qubits Ventures, that's fine since it's not a direct competitor with Western Asset. Qubits Ventures, we don't sign nondisclosure agreements, so that would be, like, a legal constraint. But if you want to keep a decent reputation as an investor, you would never try to screw over one of your companies. You don't send out their pitch deck to a bunch of people they don't know or something like that. There are certain understood ways you go about this. But, to answer your question, giving advice or making connections for companies, particularly companies I/we have invested in, is a significant part of being an angel investor or VC.
ZIERLER: Let's establish some personal history. UC Santa Cruz. It was always physics and math for you?
GINGRICH: Kind of. I wasn't much of a high school student, there weren't a lot of things that really got me interested. Right at the end of my high school career, I started getting into math and physics, I was reading some Stephen Hawking books. Finally, that clicked. I thought, "This is a pretty cool area." Then, when I started college, that motivated me to start as a physics major. I was originally going to do physics and computer science, but the first math classes I took versus my first computer science classes, I thought the math classes were more fun, so I switched to math and physics.
ZIERLER: Did you ever take advantage of any astronomy? UC Santa Cruz has such amazing astronomy there.
GINGRICH: I was never much for astronomy, so I guess the short answer is no. I was always sort of interested in the small things rather than the big things. I know enough about astronomy that most physicists would know, but I never really did any research in the area.
ZIERLER: Were you always on the theoretical physics side?
GINGRICH: I would say I leaned that way, yeah. Experiments were not as fun to me as the theory and the math behind the theory. I think the physics and the math really interacted with each other well. I would say programming using computers was always something that interested me. I did a minor in computer science when I was at Caltech. But those would be my main areas, not so much the experimental.
ZIERLER: Was there a professor or class as an undergraduate that really got you on the graduate school path or gave you the courage to think that you could go for that?
GINGRICH: I guess there were a couple. I wish I'd sat down and thought before you came here today because there were a couple in my undergrad at Santa Cruz.
ZIERLER: Did you ever take a class with Joel Primack?
GINGRICH: No, I didn't. In grad school, Preskill was starting to teach courses on quantum computing at that time, so those were great.
ZIERLER: What about the thought process of how you got to Caltech? Even if not a professor's name, the kind of classes where you said, "I'm really good at this. I can go for this."
GINGRICH: There was a math lecturer at UC Santa Cruz who would draw these beautiful pictures of the graphs. And there was a Professor Dorfan as well in physics, and we got along pretty well at Santa Cruz. He played rugby when he was younger, and I was starting to play rugby at that time, so we had that to discuss as well. That kind of kept me motivated. There was another professor at Santa Cruz who ended up dying several years later in a car accident, unfortunately, but he was very interested in differential geometry. His passion and interest in the field was pretty contagious, and that helped get me more interested in the area.
ZIERLER: What year did you graduate Santa Cruz?
ZIERLER: This is really right at the beginning of when Preskill and others are starting to think about quantum information. Did that register with you as an undergrad? Were you aware that there was this thing called quantum information? Did you apply to Caltech intending to do quantum information?
GINGRICH: I don't think I knew about it before I went to Caltech. But it was pretty soon after I got there that I learned of the field, that it existed. And my first couple years at Caltech, I bounced around to a couple different advisors. Which thankfully, Caltech is pretty tolerant of. They allow you to try a few different advisors.
ZIERLER: Before we even get to Caltech, where else did you apply? Was it Caltech or bust?
GINGRICH: No, I applied to a bunch of schools and got into a number. Harvard, I got waitlisted. I got into Cornell and was thinking seriously about that. The one that offered me the best package was UC Santa Barbara. I had some friends there. In the end, it was mostly between UC Santa Barbara and Caltech. When I was touring Caltech, thinking about different grad schools, I was walking around on the fourth floor of Lauritsen, where the theory people are, and I heard a mechanical voice coming out of one of the rooms. And it sounded just like Stephen Hawking, who I had heard on TV and whose books I'd read. I thought, "Is that Stephen Hawking?" But then, I thought, "It's a mechanical voice. Anybody with a computer could just say this in Stephen Hawking's voice. Couldn't possibly be him." I walked by and looked in, and sure enough, there he was with his entourage, and he was working with someone, maybe John. But I think once I saw that, I wasn't going to go anywhere else but Caltech.
ZIERLER: You said you bounced around between professors, but the general area would've been theoretical particle physics? Were you into cosmology?
GINGRICH: I looked at quantum photonics a little bit. I also took some classes with the CNS department, Computation and Neural Systems. It was the very early days of machine learning. I think they were even using the term machine learning way back then. But then, I heard about the quantum computing, and I did some work with some people at JPL.
ZIERLER: As a graduate student.
GINGRICH: Yes. And that was, I think, enough to get the attention of John Preskill, then I worked with him for a little bit and ended up going into his group.
ZIERLER: It's probably a fuzzy memory, but do you remember when you first encountered the term quantum information or quantum computer? Was it a seminar that John gave? Was it up at JPL? When did this become a real thing for you?
GINGRICH: If it wasn't in my first year, it was in my second year at Caltech that I was at least reading papers and seeing it. My first year was primarily classes. The second year was a lot less, when I was shopping around for an advisor. I'm sure I was reading papers in the area by my second year.
ZIERLER: And this is only John at this point, right? Alexei Kitaev was not at Caltech yet?
GINGRICH: That's right. I remember when he arrived. It was sometime when I was in grad school. I don't remember which year exactly. Maybe my third year he would've arrived.
ZIERLER: What got you to JPL? What were they doing at JPL? Was it quantum computing?
GINGRICH: That was Cristoph Adami. He was at JPL but affiliated with Caltech. He did a variety of different things, and at the time, his interest was in quantum computing. I did some work with him, then another person or two at JPL. We were just reading papers and doing something, and that was sort of my very first work in quantum computing, then I started talking to John Preskill as well. That's my recollection of the order, anyway. But I was working with both. In fact, my thesis was a couple papers that I worked with Christoph Adami and a couple people at JPL. We wrote some papers together.
ZIERLER: Was the JPL group working with John, or these are two separate groups?
GINGRICH: Somewhere in between, I would say.
ZIERLER: It's such a tiny world at that point, they must've been aware of one another.
GINGRICH: Certainly, and they talked to each other. The people at JPL were not in, say, John's group meetings, so it wasn't that close of a connection, but they talked a lot. It was a small group at JPL doing specifically quantum computing work, so yes, I would say there was plenty of communication. I would do some work with them, then I would go and present to John after group meeting or in our one-on-one meetings, that kind of stuff.
ZIERLER: Did you ever get a sense of why JPL was supporting quantum information research? There is basic science that happens there, of course. There's a long and proud history of that. But this is as far off from applied as you could possibly get circa '96, '97.
GINGRICH: It was a bit weird because most of what happens at JPL is related to some sort of space mission. And occasionally, this relatively small group–I think it eventually disbanded after I did my postdoc–tried to find applications that were more relevant to JPL's bigger mission. But I think the key was that they wrote their own grants. They got government grants, then they were just sort of–it's too strong of a statement to say they just happened to be on the JPL campus, but they were not that integrated with the space programs.
ZIERLER: Now, did John get wind of what you were doing, and he sort of invited you on board, or you approached him?
GINGRICH: Ooh, that's another one that's kind of fuzzy. I would say a little bit of both. I was talking to both at the same time. I was talking to John about being interested in the area, then I was also talking to the people at JPL. I think the original first discussion on the topic came with people at JPL, but very soon, it was both.
ZIERLER: Circa '96, '97, what was quantum information? Even then, was it about building a quantum computer? Was it just a brand new area of physics to think about? What was it to you?
GINGRICH: Shore's algorithm did exist at the time. I'm not sure what year it came out, but it wasn't much before.
ZIERLER: '94, '95.
GINGRICH: It wasn't too much before I started at Caltech. There was optimism about eventually building a quantum computer. A lot of the things that were being wrestled with in those days are the same ones being wrestled with now, namely decoherence, how to keep your qubits and the fidelity of the gates. Most of the experiments were on a smaller scale. I think factoring the number 15 was something people had been able to do. They've made notable and significant improvements in precision, but the problems are still pretty much similar to what I remember from those days.
ZIERLER: Was your sense that at this point, John was fully invested in quantum information? Was he still doing cosmology, particle physics, the stuff he was well-known for before he got into quantum information?
GINGRICH: I wouldn't want to speak for him. My memory may be a little fuzzy, so if he contradicts this, I would go with what he says. But I remember that he would occasionally try to write a paper that might be interesting to his old particle physics friends. But if I remember correctly, when he put it on the preprint, they made him take it over and put it in the quantum computing section. He was mostly switched over to quantum computing in those days.
ZIERLER: Did you have a sense, even as a graduate student, that he was thinking along the lines of formalizing this into what would become the IQI?
GINGRICH: I knew this was his area and he was looking for funding. As far as specifically the IQI, I don't remember worrying about that too much. I was more worried about reading papers, coming up with a research project, trying to write papers.
ZIERLER: Did it feel more formalized than sort of a professor with his research group? Meaning that you were part of something that was building into what would become the IQI? Did it feel different than a graduate student experience where it's the professor, and this is his research group?
GINGRICH: That's an interesting question, but it was my first time being a grad student, so I didn't really know what other groups' experiences were.
ZIERLER: How big was his group?
GINGRICH: I think we had five or six, something like that, at the time. It didn't feel to me as though it was growing rapidly for the few years that I was there, however, he was bringing in more and more postdocs, then Alexei Kitaev came over. Even though I may not have been thinking about the growth at that time, when I look back, yeah, I guess he must've been getting more funding to bring in all these extra people.
ZIERLER: What was his style as a mentor? Would he hand you problems? Would you come up with them on your own and then go to him for approval? What was that dynamic?
GINGRICH: I'll contrast him to some of my friends that were in chemistry. They had to come in and be in the lab all day long, and they were told exactly what they needed to do. They were a workhorse working on what the professor told them to do. That was very much not John's style. He wanted you to find something that was interesting to you. He would help direct you to the right people, and he expected you to go, reach out, and talk to that person, and start doing some research on your own. I remember at first, it was kind of nerve-wracking because I was coming from classes where I was always told what to do, and now someone says, "Do what's interesting to you. Come and check in with me from time to time," I wasn't quite sure how to do it. But I knew it was also what I had wanted the whole time, so I definitely didn't want to waste that opportunity. I did go out and talk to people, including the people at JPL, which I ended up writing several papers with. Then, he would check in on me, and we would have these group meetings. I remember the group meetings would be three, four hours sometimes, which at the time, was a little bit torturous.
ZIERLER: Are these the Wednesday night pizza dinners that I heard about?
GINGRICH: I forget which day of the week it was, but probably his whole group would get together, someone would present on their research, but then other people would start talking. The conversations might end up going on for a few hours. It could be torturous when it was somebody else's research. And I remember this being sort of John's reputation at the time, that you'd present to him a paper, and he would ask all kinds of questions, and some of the questions would seem irrelevant, and it might feel kind of annoying, and it would take a long time. But by the end, he understood it better than you did and told you exactly why it didn't work or the next area of research that you should have thought of yourself. He asked a lot of questions, and at the end, he generally understood it better than the author.
ZIERLER: The historian's perspective looking back at John's research group in the late 1990s, this is really the dawn of the field. This is where it was happening. Did you have any sense of that in real time, that you were part of that? As opposed to, say, going in to particle theory, which has been around for 50 years in its modern form. Did you sense the newness of it in real time?
GINGRICH: Let's say I should have. [Laugh] But I was thinking about what was right in front of me. It was a very interesting field to me.
ZIERLER: Maybe I can frame the question like this. To go back to particle theory, when you're figuring out what your dissertation topic is going to be, there's a vast literature and history of discovery about what's already known and out there. You really have to be strategic in trying to add to this body of knowledge. Did you feel like quantum information was all wide open, that you could've taken on any number of particular topics, and it all would've been brand new?
GINGRICH: I would say there were a lot of promising and young areas. I used to kind, in a flippant way, say I was working on writing algorithms for a computer that doesn't exist. [Laugh] I would spend time trying to come up with what may be a useful algorithm, even though we didn't know how the computer would be implemented. It was a new and promising area. From my perspective, when I went into it, I was hoping this sort of private funding ramp-up would happen sooner because I kind of wanted to go into a private company. And I even talked to a few before I graduated. One was working on quantum cryptography. Then, 9/11 happened, and everything they were doing was made top-secret. And that was part of why I eventually moved over to finance. I knew I wanted something more immediately applicable.
Even though there was all this great potential in quantum computing–and even at that time, it felt like there were some things people overhyped. People were saying, "We can make this quantum computer. We're going to make it next year." Then, you go in and look at the math. I remember we looked at one implementation using half-silvered mirrors and did some calculations with how precise they'd have to be to do any kind of real calculation, and then how precise the actual ones are. And people have been doing optics for many, many decades, so people have really, really tried to make the best possible half-silvered mirror. But there were still several orders of magnitude before you could start to do the calculation. Yet sometimes, it was advertised as coming the next year. I know it would be a ways off. But still, an incredible amount of potential.
ZIERLER: What about specifically with error correction? Did people in the group have an appreciation of how difficult it was, and maybe have a sense of how difficult it would be 20, 25 years into the future?
GINGRICH: There were some clever algorithms that had arisen by that time. I think Daniel Gottesman was there at the time. That wasn't what I specialized in, but I did learn about his work, and it was very interesting. I would say I thought of the study of quantum computing and quantum information as probably, from a physicist's point of view, more of a good way to find new physics.
ZIERLER: People were talking about quantum computers as a tool for fundamental physics.
GINGRICH: Right. And I thought that was a compelling way to look at it. Either we can make this quantum computer, or if we can't, we're going to discover new physics, that the world works differently than we thought it did.
ZIERLER: Like quantum gravity, for example.
GINGRICH: Yeah, but I would say more like the way that measurement works. Maybe measurement doesn't quite work the way we mathematically represent it as a collapsing of the wave function. You would have to either learn better how that works or get a quantum computer at the end, and either one's a good thing, so study this area, and see where you can get to.
ZIERLER: A decade later, of course, the M, matter, is added to IQI, IQIM. When you were there, this is a purely theoretical operation. But were people starting to think about the engineering and even the condensed-matter aspect of building a quantum computer? Or was that too far afield?
GINGRICH: People were. I remember Jeff Kimble was there, and he was doing some interesting experiments in the field. Other things I recall, after I'd left the field, I think some of the concepts of multipartite entanglement had found more uses in condensed matter physics. Part of my thesis was on multipartite entanglement. I don't think they actually used my research, but it was one thing I was looking into. And I'm not a researcher in this field anymore, but there have been applications to information destruction in black holes. I think I remember some discussion of that when I was there at Caltech. It wasn't an area I did research in though.
ZIERLER: How did the papers that formed your dissertation come together, and was there a unifying theme between the papers?
GINGRICH: It was kind of a few different areas. It was the multipartite entanglement that I was describing…
ZIERLER: What does multipartite entanglement mean?
GINGRICH: Entanglement is pretty well understood between two systems. You can measure it–EPR pairs would be sort of maximally entangled, so you can kind of say, for different states, "How many EPR pairs could you get from a large number of these?" And there were some pretty established ways of at least categorizing or ordering the amount of entanglement between states. But when you have, say, three different systems, there's the entanglement between any chosen pair, but there's also an extra amount of entanglement that is sort of among all three that is distinct from all the individual pairs. That would be multipartite entanglement. That was part of my thesis, finding ways to try to measure that. I think it doesn't fit into as nice of an ordering like you can get for two-part entanglement. But still, there were some attempts to look at that. And then, also algorithms. I wrote some papers on ways to potentially improve Grover's algorithm, make it maybe more practical if you don't, say, know the number of solutions. Some tweaks on how Grover's algorithm worked.
ZIERLER: As you mentioned, you spent time at JPL throughout. You did some of this work at JPL.
ZIERLER: Were any of John's other students also doing this, or you were unique in that regard?
GINGRICH: I don't know if anyone had, at that time, two legs as much in the two groups, but there were others that were working with them. There must've been a paper or two that came out of it with the people at JPL and others of John's students. From my recollection, I would imagine I had the most sort of cross-papers at that time, but I think there were a couple others, too.
ZIERLER: Who else besides John was on your thesis committee?
GINGRICH: Steve Frautschi. He's since retired. I actually have my thesis here in my bookshelf. [Laugh] I don't know if it says my thesis committee. Entanglement of Multipartite Quantum States and the Generalized Quantum Search. But yeah, Frautschi. I don't think Mark Wise was on my committee.
ZIERLER: Did you interact with Kitaev at all as a graduate student? Was he accessible?
GINGRICH: I certainly saw and talked to him, and he asked questions when I was presenting at the group meeting and stuff, but we didn't write any papers together.
ZIERLER: The postdoc at JPL, was that just a very natural and easy continuation of what you had already done? Were you considering postdocs elsewhere?
GINGRICH: I looked at a couple places. In fact, John recommended I work at the Perimeter Institute. Probably for a career in quantum computing, that would've been a better place to go. But I was already not sure if I wanted to stay in quantum computing, and I had a lot deeper roots in LA. Since I already knew the people at JPL, it was easier to do a postdoc there.
ZIERLER: Had you already talked to Mark Wise about the investing angle, and if so, was that part of the interest in staying there? Or that came during your postdoc?
GINGRICH: During the postdoc. The postdoc was about a year and a half. Six months or a year into it, I decided I needed to look at some other areas. I almost certainly would have gone into tech in the Bay Area if it weren't for the timing. This was right in 2001, so right after the dot-com bubble burst. And it took about two, two and a half years for that bubble to fully burst. After another year, year and a half at JPL, I was just convinced that the tech industry was dead. I clearly was wrong. [Laugh] But to me, that's what it felt like. I'm like, "I'm going to have to let go of the dreams of going to a tech company and think about what else I can do."
ZIERLER: Did you go on the academic job market at all? Did you give job talks? Were there even jobs in your area of expertise? That's another problem with being in a path-breaking field. How many academic departments were advertising in quantum information at that point? None? A few?
GINGRICH: Yeah, they were pretty few and far between, and it was pretty sure that you had to do a postdoc to get a professorship somewhere. The postdoc I was doing, I thought, "This is good, I can get more papers and feel things out for a couple years." I don't recall applying for a professorship at that time.
ZIERLER: What about industry jobs in quantum information? Was that even a thing in 2002, 2003?
GINGRICH: Not much of a thing.
ZIERLER: Alexei came from Microsoft, so there was a tiny operation there. Was there anywhere else?
GINGRICH: That's what I was going to bring up. Microsoft was the big tech behemoth at that time, and they had a group that was doing specifically quantum computing. That's the only industry group that I can think of.
ZIERLER: Just for context, was there even an Amazon? Forget AWS, I don't even know if there was an Amazon.com at that point.
GINGRICH: I think they may have existed, but they dipped in the dot-com bubble.
ZIERLER: And certainly, Google was not doing anything in quantum information then.
GINGRICH: I'm going to agree with that statement. I don't have absolute proof of that, but I don't think Google was doing anything in quantum. And they weren't the behemoth, I think they were almost still–they started out as sticking only to search, until they grew really big, then they branched out.
ZIERLER: There's Lycos, there's Fetch.com, there's Ask Jeeves. Google was not the dominant player in search like they are now, obviously.
GINGRICH: My friend worked at Snap.com, another search engine that wanted to beat Google, but eventually closed down.
ZIERLER: Daniel Gottesman had to do several postdocs before he landed an offer because of the same issue. Basically, the choices were narrow in terms of a career path at this point.
GINGRICH: Yeah, and as I mentioned, I did find one company. I talked to someone who was associated with or could get me in contact with this company that wanted to do encryption. They wanted to commercialize quantum key distribution. Which, by the way, has happened by now. But 9/11 happened, so that company was no longer an option. And a couple years before I graduated, I talked to a number of Bay Area companies, and at that time, they were all gung ho, like, "Oh, yeah, definitely. We can't wait to hire you." But by the time I graduated, they had all fired half of their staff. It was better to do the postdoc. But because there weren't a lot of positions, you had to do a lot of postdocs, I was not really looking at an academic career in quantum computing.
ZIERLER: What was it like when you talked with Mark about his interests in business? What were those interactions like for you?
GINGRICH: It wasn't so much business as the quantitative derivatives and structured-product modeling, which was a big and growing area of finance in those days. I started to look around, started reading books. Someone actually said I should look at Pimco because they were big and growing their financial engineers who do this kind of modeling. I did that, I did some Google searches, and I found this guy, Vineer Bhansali, who was associated in some way with Mark. I think they had written some papers together. I think I found that through a Google search. I saw Mark Wise's name and was like, "I remember him from grad school." I think I asked John Preskill, "Do you know Mark?" Either John suggested it or I suggested it. I talked to Mark a little bit about what I was interested in, then we started a research project with Vineer Bhansali on structured-product modeling. Started discussing it at first, and then he was able to hire me on as a postdoc.
ZIERLER: What was the funding for that? Was this, like, NSF-supported research? Private capital?
GINGRICH: I don't remember. I'm sure I was told at the time, but he was able to get funding for a postdoc. I don't even want to try to guess because I'm sure I'll remember it wrong, but he was able to find funding somewhere for this brief postdoc.
ZIERLER: Did it click for you right away that this was going to be your path, this was viable?
GINGRICH: When I found the mathematics interesting and heard that it was a growing field, I liked that. And I had always had an interest in business. I guess I wasn't quite sure how to express it or hadn't taken classes on it. But my dad was an MBA and used to always read Business Week and stuff, so I had at least an underlying interest in the area. But I think after grad school, when I started looking at the mathematics that they use in quantitative finance, like analyzing the yield of a bond, the yield of a certain set of cashflows, how you determined whether one was better than the other using a couple different mathematical techniques, it just seemed interesting to me. It was the kind of stuff I was thinking about, and it was in a growing field. And like you mentioned, there weren't a lot of professorships in quantum computing, I'd probably have to move somewhere far away and work as a professor. But working in quantitative finance, it would be in a big city, which is what I wanted, and it'd be more of a corporate job which is what I was looking for.
ZIERLER: The field of quantitative finance was well-developed at this point. This goes back to Jim Simons and Renaissance. This was already a mature area? Or did you feel that this was new also?
GINGRICH: I would say the kind of stuff that I do kind of had its beginning–Emanuel Derman. He was a physicist who went over to Goldman Sachs and developed some of their modeling for interest rates, derivatives. I kind of see him as one of the early ones. Gosh, it would've been, I don't know, maybe five to eight years before I got into the field. It was growing. These big banks realized that this quantitative modeling was very useful. You could sell these exotic instruments, and then you could hedge your risk with other instruments that were out there in the market. And if you did it well, you could make a lot of fees in the meantime. I would say an overdevelopment of that was a major contributor to the global financial crisis that happened in 2007, 2008.
ZIERLER: What was your first job out of the postdoc?
GINGRICH: After the finance postdoc, I worked at Pimco. And I was modeling derivatives, but also structured products. That was sort of the big thing that contributed to the financial crisis. Pimco was quite conservative in that space, meaning that they weren't one of the big issuers. I think Bill Gross kind of wanted to stay away from that stuff. Bill Gross was, at the time, the head of Pimco. It was one of the largest fixed-income managers out there. I was modeling structured products. And the banks would've had armies of people doing this, so my job was to just get some models together so we could check the banks' models. And when we would build a CDO, we could check the bank's pricing so we didn't have to just take whatever value they gave us. And those kind of blew up in the financial crisis.
ZIERLER: Did you see it coming at all? Was there anything that smelled off to you, especially with real estate?
GINGRICH: I feel, at least to some degree, a responsibility for it. I was a junior researcher, so I was not the one deciding to put $100 billion…
ZIERLER: You're not the CEO of Countrywide. [Laugh]
GINGRICH: Right, I wasn't the one that made these grand decisions, but I was modeling the securities. When I think about it, I can at least see several instances where I bubbled up, raised issues about the modeling that did end up being very material to the severity of the financial crisis. Namely, the rating model. We got some papers about how the rating models were rating these different tranches. It was assigned to me, "Read the paper and reconstruct their methodology." But after I read the paper, I was like, "This is terrible." From a scientist's point of view, they did something that would be logical, they used two different methods to calculate the correlations between the different companies, but the two different methods came to different answers by an order of magnitude. Like, one would say the correlation was 8%, the other said 80%. Hugely different results.
They just took the average of the two and used that. I raised that up to my manager. I'm like, "They're rating this, and people are deciding whether to lend someone huge amounts of money based on this rating, but the methodology is very sort of stuck together with duct tape." Additionally, you managed the different tranches of the CDO, looking at the sensitivity to other instruments, I noticed that some small changes in the methodology could result in very large differences in sensitivities. And I bubbled that up, too. I said, "These are two very reasonable ways to do it, and they come up with very different answers. The banks are using this and hedging large amounts. This is worrying. There should be large error bars on this." I can at least say I bubbled those two things up.
ZIERLER: Was whistleblowing even an option? This is obviously before Dodd-Frank, which is reactive to all of this. I'm thinking of the Hollywood portrayals of this, The Big Short, Margin Call. Were there people who were, like, screaming, "This is a problem," before the crash? Or you were looking at these models, and something didn't feel right, and it's as quiet as that?
GINGRICH: No, there were. And in particular, the people who made some of these models, like the Gaussian copula model and people who used it were saying, "Hey, this is just kind of an estimate first guess to give a structure to the problem. The model hasn't really been tested in extreme environments." My view on the financial crisis is, like a lot of people, I think rightly, blame a misuse of these quantitative models for exacerbating it. Essentially, spreading the credit risk too much, allowing people to take too much leverage is kind of the ultimate issue that happened. And these quantitative models helped with that. But I think many of the people who really dug into the nuts and bolts said, "Hey, these are just estimates." But it was so profitable that if you refused to do it because you said the model wasn't reliable, somebody else would do it. And this would more be at the banks. I think Pimco was quite responsible, actually. Like I said, they were a very limited player in the CDO market.
But just from being in the field, I know how many people were working on it and what kinds of discussions were had. But if you said, "I refuse to give a number," they'd just say, "All right, we'll get that guy to do it." And I think that's the nature of any banking crisis, underwriting crisis. You're making money by taking risk and borrowing more money. Whoever's coming in and saying, "Hey, we should put on the brakes," they're not making money, and everybody else is making money, so their voice goes down, and the other voice goes up. I think that's the nature of any credit crisis like that. And it was able to be covered up by various regulations and derivatives, and over-reliance on ratings, and things like that. We were able to unfortunately kind of cover up the fact that there was a lot of borrowing going on, more borrowing than we thought. And then, it gets corrected when finally, people start losing money, then everybody panics.
ZIERLER: What did the crisis mean for Pimco, what did it mean for you?
GINGRICH: As I said, Pimco was quite wise about the crisis. They did not invest a huge amount in the more esoteric–I would love to say it was my advice that added to that, but I doubt it had much to do with it. But at least some of my advice was in the right direction. Pimco grew, so that was good for me in general.
ZIERLER: You were okay, the company was okay through '08, '09.
GINGRICH: Yeah, Pimco grew after the financial crisis because they really had avoided most of those losses that many of their competitors had not.
ZIERLER: Did you grow at Pimco? Did you have increasing responsibility over the years?
GINGRICH: Sure. I kept getting raises. There was a lot of churn at the top management level. My responsibilities changed eventually. They did less of the derivatives modeling that I had specialized in. They had a big hedge fund run by an ex-physicist, Changhong Zhu. When he left the firm, they were doing less of that kind of work. I ended up doing more–to me, it felt like more IT work, but it was also modeling work, just kind of keeping these models that were working on 20,000 different securities, making sure those worked. And that's when I switched over to Western Asset. I knew Western Asset was building this in-house risk system. I talked to a number of different places, but Western Asset made the most sense.
ZIERLER: How much of a leap was it when you went to Western Asset, just going into risk? Were you doing risk at Pimco at all?
GINGRICH: Yeah, I was more on the single-security modeling side of risk at Pimco. And also, quantitative portfolios to some degree, like tail-risk portfolios, I was working on that at Pimco as well as a couple other quantitative types of portfolios. Switching to Western was a bit of a change because I was looking more at the portfolio level instead of the single security level. But I was still using those derivative models, getting those working at the firm and producing sensible numbers.
ZIERLER: Was Western Asset a competitor to Pimco?
ZIERLER: Did you know people at Western Asset already? What was your point of connection there?
GINGRICH: I don't think I knew anyone at Western Asset. I knew of them as a competitor to Pimco. There was another person at Pimco that switched over at about the same time, and I had done some work with him, especially in the structured-product CDO area.
ZIERLER: What was compelling to you to make the move?
GINGRICH: When I started at Pimco, I knew I was good at math, but I didn't really know much about investing. I used that math to do derivatives modeling, but I realized just knowing how the derivatives worked wasn't good enough. I needed to follow and understand the markets, what was moving the markets. Over about six years, I felt like I'd gotten to the point where I did understand the markets quite well, and I could speak intelligently about them. But I felt like almost my quantitative and programming skill was holding me back, that they were always going to think of me as the person who fixes the programming problems, and not the one making decisions to buy and sell. I wanted to be looking at things at the portfolio level and from an investor perspective.
ZIERLER: It was a step up in some ways.
GINGRICH: It was the types of responsibilities I wanted, or that I thought I wanted, at the time, and mostly, my expectation was accurate.
ZIERLER: The separate line in your career, venture capital and being an angel investor, were you already doing this at that point, or this was a more recent development?
GINGRICH: No, it was after around 2010, when the financial crisis was finally getting better. I remember there was a lot of venture capital before the financial crisis, and it all kind of went away. I thought, "Well, it should come back. Now, the financial crisis is done. It's been a few years." I really was looking to do private investments. I had money saved up from working, and I wanted to do something that was interesting to me, and private equity, private assets was interesting to me, like small companies. It was around 2013, 2014, I started talking to the Pasadena Angels, an angel investing group and some friends from grad school that had started companies, and I made some investments in that space. Then, I joined the Pasadena Angels and continued making more investments, either through the Angels or through other areas. And these are generally small. These are $20,000, $30,000 checks to different companies.
ZIERLER: Were you following developments in quantum information, metrology, for example, things like that?
GINGRICH: Yeah. When I first started back in 2013, 2014, not as much, but 2015, 2016, I started. I even thought, "Would it be possible to find a job in the quantum computing space?" Just because I thought it was interesting, and it was growing. By then, there were a lot of companies, a lot of conferences specifically on quantum computing. I went to a couple of those conferences. I did end up investing in QC Ware, which is one of those quantum computing startups, and they have a big conference every year. Google had a conference that I went to, I met QCWare there and worked on a white paper with them. I did a little bit of early-stage research. The white paper was on a financial application of quantum computers. I saw some posters of algorithms people were proposing, and they had some that were supposed to be applied to finance, but I was like, "No one has ever worried about the problem that they're trying to solve." [Laugh] But it was similar to some problems I knew we had worked on at Western and at Pimco.
ZIERLER: In not making that jump and going fully into quantum computing, I wonder, did becoming a dad make you a little more risk-averse? Did you find making that move risky?
GINGRICH: Yes. Ultimately, I have a built-up background in the field of quantitative finance, and I don't have one in quantum computing. I just simply wouldn't be worth that much to them. It really wouldn't have made any sense. Even the few places I talked to, I talked salary, and it didn't make any sense. It was much better for me to stay in my current job, spend most of my time doing that, do quantum computing on the side. If I'd switched five years earlier, maybe I would've had a built-up background that would've made me worth more to those companies.
ZIERLER: You mentioned at the beginning of our conversation that there's been a dip in industrial support for quantum information. There is a ramp-up from 2015 right through to 2021. What were you following remember? What was happening that explains this burst in interest? Was there a technical breakthrough? Was it sort of FOMO in the tech sector?
GINGRICH: I can only speculate. A few things I touched on before. The problems they're solving now really are very similar to the problems we were solving back in 2001. They have definitely made some progress. I think D-Wave was the first company to really go public, and I think they were able to, especially in the early days, raise good money in the Bay Area. And I think people started to copy that and started to get more enthusiasm from the private sector for funding. As far as exactly why it grew, I'm not sure. But it definitely grew.
ZIERLER: What about the idea that there were these offshoots from the main line of research that were promising, quantum control, quantum measurement, things like that, where you saw potential in profitability? Would that be something that would explain this burst of investment in this area also, that there were glimmers of, "This is an area where people can make money"?
GINGRICH: I still think one of the ones that gets people most excited is Shore's algorithm, which was around for a while. If someone comes up with a way to break encryption, that's huge. And whoever owns that, the world has to come knocking at their door and asking for help. And people have made more progress on the actual physical implementation. The gates are getting higher fidelity and longer-lasting qubits. I still think people put a lot of hope in the Shore's algorithm breakthrough. I think it's one of the later ones that actually gets done because it does require very large-scale coherence. But I think it's the one that scares people the most. I wish I could come up with a real cause for why it's become bigger.
ZIERLER: When COVID hit, what did that mean for you? Were you already working from home? Or that was a jolt in terms of your workflow? And what about for finance in general?
GINGRICH: For the most part, especially in my field, it was pretty traumatic. It was a scary time.
ZIERLER: You must not have had this kind of setup.
GINGRICH: No, I think I had a laptop I would use at home, but I didn't have a work laptop. And I would almost never log in from home. I went into the office. The trade floor, especially within my world of fixed income, is where a lot of things happen. That's where all the portfolio managers are, they're talking to each other, they sit next to each other. They purposefully don't put walls between people so that you can overhear conversations, and things happy quickly. COVID happens, and suddenly, everyone who has never worked from home before has to work from home. And for the banks, I attribute much of the chaos within the markets, which I think we've all tried to forget about since then, but when COVID first happened…
ZIERLER: I saw my stock portfolio. It was scary.
GINGRICH: Yeah. But it wasn't only stocks. It was a lot of different things. A lot of correlations that had been pretty reliable before then broke down. VIX, which is probably the best measure of fear in the stock market, went from close to all-time lows before COVID hit to making new all-time highs. You haven't seen that since the Great Depression, and even then, you didn't see it so quickly. Maybe a couple single days, like the crash of '87. That might be one exception where it came on very fast. And I attribute a lot of that chaos to so many people in the markets who were used to working one way having to work another way. The people keeping all these correlations going by their trades suddenly weren't able to do it or do it as quickly. Western Asset's portfolios, the ones that I'm the risk manager for, their volatility went way up, so there was a lot of analysis to do. And I had to do it from home. So yeah, it was a pretty stressful time period. [Laugh]
ZIERLER: How'd you adjust? Was it pretty quick, or it took some time?
GINGRICH: I adjusted because I had to. Everybody had to. It was just as tough for everybody else. Over time, it got easier. We got a little more used to working from home, trying to take care of the kids from home. My son pulled out his tooth on his first day of Zoom class right in front of the teacher. She couldn't do anything about it. [Laugh] But as with everybody, eventually we adjusted. It is nice to work at home, but I certainly admit that being in the office and able to have those spontaneous conversations is really where new ideas happen.
ZIERLER: If you would connect what was happening in 2020 and 2021 as a response to COVID and the sort of decrease in investment in quantum information, there's a connection here. There must be. Why else would we not see a steady increase in interest and investment in quantum information from 2021 to now?
GINGRICH: Quantum computing, quantum information generally would be considered part of tech investing, in my view and I think most people's views. Actually, directly after COVID, there were all this government stimulus that came in, and suddenly all these companies like Amazon and Google are much more popular. Actually, there was a huge growth of tech investing. 2021 was a great year to be involved in tech investing, including quantum computing. There were a number of quantum computing companies that went public in 2021. there was just a lot of enthusiasm for tech. In fact, it felt a little scary that there was so much enthusiasm for it.
ZIERLER: A bubble maybe.
GINGRICH: It can't last forever. Although, having said that, pretty much everything past the financial crisis has been growing tech enthusiasm, and there were many times it felt a little scary then, too. Maybe too much enthusiasm. But 2021 also felt that way. Then, when inflation hit and interest rates shot up, there were a lot of different mechanisms people discussed for why it reduced tech investing, but I think it makes people feel less wealthy. And suddenly, you're not forced into risky assets because you can't earn anything on cash. Now, you can actually earn decent money on cash. Interest rates went up a huge amount in 2022. Historically, more than they've ever done, especially if you take into account that they started from almost zero. If you go back to the 70s or 80s, there were some big moves, but you're starting at an interest rate of, like, 14%. A 1% move from 14 to 15 is a lot different from 0 to 1 for an interest rate move. Since then, there's been a pretty big decline in tech. 2022, pretty much tech was going down most of the year. When ChatGPT came out, that has brought back tech as a sector and really gotten people's enthusiasm for technology in general up. I imagine ChatGPT has helped quantum computing to some degree, although it's mostly helping companies that are more directly related to producing these large language models. But many of those quantum computing companies that went public in 2021 are well below their IPO price.
ZIERLER: Has this made you more cautious as an investor?
GINGRICH: I try to keep my caution level constant, I guess I'd say. [Laugh] To not overreact to market movements.
ZIERLER: Because it's really about the science. Profitability is where the science is legit. It's decoupled from the trends of economics and finance. Is that fair?
GINGRICH: Kind of. For private financing, that's definitely related to the market. The science may still be going, but as far as private companies, and how well they're going to take off, and how they're going to be able to hire people, that's very dependent on the whims of the market. I guess I try not to be overly aggressive in the good times so that I don't have to sell things in the bad times. I'm maintaining my positions as much as possible. I still think they'll come back.
ZIERLER: Steady as she goes.
GINGRICH: Yeah. I don't know what will be the next big thing, but I try to have a little bit of exposure to everything.
ZIERLER: We'll bring the story right up to the present. The interest that you expressed to me in moving over to Western Asset from Pimco. How close are you to those new responsibilities and that learning curve that you took on? What are you still doing from that original appointment, and what's been brand new to you as you've grown in your career these past 10 years?
GINGRICH: The original thing I was hired for was building the WISER risk system, the internal risk system. And that's mostly built. The firm has not been growing that for several years now, however, keeping it running is plenty of work. I also have QIS (Quantitative Investment Solutions) responsibilities, other quantitative investing ideas. We started a tail-risk fund that also grew to billions, at least the main portfolio that we had a tail-risk overlay on. That one has closed down. We have some other quantitative strategies that we're working on.
ZIERLER: Then, on the investing side, as an angel investor, VC, what are you seeing? When you make decisions now, what are the trend lines that you're seeing?
GINGRICH: I think people don't have as much money burning a hole in their pockets right now, I think there are more opportunities out there. But especially if you're a seed or pre-seed investor, you eventually want the ones that write multimillion-dollar checks to invest in your companies. You need them to be investing, too, because that's usually going to be the next stage, but many of the large VCs have dialed back. Where do I see things going? I think if you have the money to invest, you can be more discriminating now. I would say in 2021, the entrepreneurs really were in the driver's seat. They had their choice of people willing to write them big checks without much due diligence. Now, you can ask for more due diligence. They don't have their choice of investors. That's good for us. You can find a good opportunity if you're willing to do the work. That's how I see the field. We don't know how long this recent downturn will last, but you can get much better deals now than you could've gotten in 2021.
ZIERLER: The economic reporting, when you look at the fundamentals, the economy now is pretty strong in terms of employment, consumer confidence, inflation. It's certainly been a lot worse than it is right now. The difficulties that you're articulating, is that to say that this field is somewhat decoupled from those broader fundamentals in the economy?
GINGRICH: The last couple years have been interesting in that it's almost been more of a stagflation environment. Inflation went up a lot, then interest rates went up a historic amount and historically quickly. For at least a decade before that, even small interest rate increases were seen as really bad for stocks, really bad for the economy. Everyone assumed that things would shrink, like the housing market would dry up. But it hasn't quite happened, and that's the surprise to certainly not just me. There were a lot of people who predicted we'd already be in a recession at this time of year. But we're not. Amazingly, we just had a GDP number that just came out at almost 4%, which is quite good. And I think people are trying to figure out why that is. The stock market has not done well, basically been flat for the last two years. Which is rare. We were making about 10% on average for pretty much the decade after the financial crisis. To be flat for two years is not great for stocks.
I think it appears the economy is moving away from tech and into old-economy types of things. Energy is doing quite well, whereas tech is kind of stagnant, except for ChatGPT-related stuff. Those are the surprising things that are happening right now. I guess the big question is, how long will it last? I wish I knew with any kind of certainty. I guess I'd say the dot-com crisis lasted about two and a half years of downturn. The financial crisis was about one and a half years. The onset of COVID was only about one and a half months. We're now almost two years into this sort of slowdown. We'll see if eventually, the higher interest rates lead to the demand destruction that they're sort of meant to do through some sort of recession, but it hasn't happened yet. There are many opinions out there. I won't go into all of them because I'm not sure which one is right.
ZIERLER: Now that we've worked right up to the present, for the last part of our talk, a few retrospective questions about your career, then we'll end looking to the future. First, on the Caltech side, have you remained active? Do you go to alumni events? Do you keep in touch with your fellow classmates, professors, things like that?
GINGRICH: A bit, yeah. I just went to a Watson lecture a couple of days ago. There's a Caltech Entrepreneurs Forum, which has an association with the Pasadena Angels, so I go to those events. Lately, there are five or six of them a year, something like that. I taught a class on machine learning and finance at Caltech. That was 2019. I'd love to do that again, but to do that along with a full-time job and my other things, it was just too time-consuming. Oh, and the rugby team. We had a rugby reunion, people who played on the Caltech rugby team. We had I think at least 50, less than 100, guys come in from all over the world. Some were in Ireland, various parts of Europe. They flew in, and we played some touch rugby, hung out, drank some beer. Off and on, we've played touch rugby. There are a few of us who still live within driving distance of Caltech. When we can get enough players, we'll play on Thursdays.
ZIERLER: Have you ever served in a mentor capacity in the course of your career for graduate students who followed a similar career path to yours? Have you seen other people come out of physics or even quantum information programs and make the pivot that you did into finance and investing?
GINGRICH: I would not say, like, a formal mentorship. But every now and again, somebody in physics reaches out to me. In fact, just this week, I had lunch with someone who reached out to me on LinkedIn. He's currently a grad student at Caltech and working in quantum stuff, and he had seen my name and was curious about switching into finance. I had lunch with him and gave him some advice on the difference between the fields, how to make the switch. I have meetings like that every now and again.
ZIERLER: What are some things that have stayed with you from your Caltech days? Beyond the science, things like the approach to data, problem solving. What are some bedrock Caltech educational values that you've drawn on over the years?
GINGRICH: In the Preskill group meetings, those were one of the few times I've felt outclassed in a mathematical perspective. After that, I would say I've never felt that way. Probably, I'm just like egotistical. [Laugh] I don't usually feel outclassed, where I would have thought about something all night long, and I tell somebody about it, and they solve it right in front of me. That happened a few times, I remember, at Caltech. Being around people who really thought deeply about how the mathematics actually worked and why, I think that's what's been beneficial in my career since then. Don't just think of it as symbols on paper. Understand what's going on. Break it down in various different ways. The two fields are quite different. I like to say that it's a matter of precision. In physics, you can get incredibly precise measures. You can have things like the fine-structure constant, which we know to, like, one part in a hundred million. Whereas in finance, any kind of prediction you do, if it has a 10% correlation with what happens in the real world, that's great. But that would be incredibly low in Physics. The error bars on any kind of prediction in finance are massive.
ZIERLER: That's because you're inserting humans into the equation, right? Behavior, society, everything.
GINGRICH: Yeah. Human behavior doesn't lend itself to a precise mathematical description. You can sort of generalize, and it's certainly important. There could be billions of dollars on the line to try to make some reasonable generalization about what humans are going to do, but it's never going to be very precise. Whereas electrons and protons will do the exact same thing every time, and you can apply these great symmetries and get incredibly exact answers that you just never get in finance.
ZIERLER: The road not traveled, had you gone down the academic path and continued with the research you were doing at Caltech and JPL. Have you seen others pick that research up? Has there been an ongoing life to the research that you pursued as a grad student and postdoc?
GINGRICH: I can only say, from a 10,000-foot level, people have still worked on the types of things. My first few years, especially the years I was at Pimco, I didn't follow the papers very much. If there was much built specifically on the papers that I wrote, I wouldn't know the lineage, at least. But the multipartite entanglement, I know that's gotten some interesting applications in condensed-matter physics. There are all kinds of modifications and variations of Grover's search algorithm. We look at it for doing Monte Carlo searches in finance. And that's basically a modification of the Grover search algorithm. Finding ways to kind of use it in different contexts has certainly grown, and that was another thing I did in my thesis. Another area I wrote a paper on was relativistic quantum information. I did that at JPL. That was a very interesting area, although now that I bring that one up, I can't think of applications of that at this point. It's an interesting theoretical idea, but it hasn't led to something practical that I know of.
ZIERLER: Finally, we'll end looking to the future. I'll ask a question on the business side and a question on the science side. Because you come at business with a foundation in math and science, what keeps it interesting for you? For a scientist, there's always more to discover in nature. Bringing that headspace into the business world, what keeps it fresh for you?
GINGRICH: Well, one nice thing about finance and the business world is, it's constantly changing. There are constantly new problems coming in that need someone to analyze them. And I like that. I like showing up in the morning, you don't know what problem's going to hit you, and you may have to do some fairly deep analysis in just a couple hours to get an answer. It's fun and interesting to watch where the markets are going, to think about where they will go, to think about all the different players, how they're moving their money around, trying to understand what they will do in the future. And communicating that to people who are trying to decide how to use their money. That keeps it fresh and interesting. In science, you've got sort of a stationary target, and you can spend forever trying to answer a couple simple questions. And if you do answer it, the world has more knowledge. And that lasts forever. In finance, there are a lot of little problems coming up, and someone else may have solved it before, but it sort of needs to be solved right now. And unfortunately, what you solve today may not be applicable in six months, when the world is different. But the world doesn't change from a physics point of view. It does change from a finance point of view.
ZIERLER: We'll save my hardest question for last. Whenever it happens, when we have the scalable quantum computer, both on the scholarly side, what you've achieved in Caltech and JPL, and in your support for these companies working toward that goal, what do you see as your contributions?
GINGRICH: On the quantum computing, quantum technology side, I would love to help people who have a pool of money that they would like to invest in technologies and to help them find the right ones to get the money to, so that they can really move the field forward. And to help the scientists, on the other side, to take what may be very interesting science and make it into something practical that can be a product and a business at some point. I'd love to be a middle person, helping them. Because I know those two groups, while they're both big and important, they don't always talk to each other well. The deep tech and people who have large amounts of money to invest in technology, helping that conversation and getting the funding in the right spots would be very interesting.
ZIERLER: And that's a real convergence point from your intellectual life and your professional life.
GINGRICH: Yeah. Alternatively, staying in the quantitative finance realm, primarily doing that and investing in some of these companies on the side, maybe just advising on the quantum technologies investing is also interesting. And that's sort of what I'm currently doing.
ZIERLER: I want to thank you so much for spending this time. It's been a great conversation. Great for Caltech history.
GINGRICH: Thanks. It's been very interesting and fun.