Let me show you how to be a good lazy and change your predictions You know I'm a big fan of everything physics, so when I heard that Bayesian stats was especially useful in quantum physics, I had to make an episode about it. You'll hear from Chris Ferry, an associate professor at the Center for Quantum Software and Information of the University of Technology, Sydney.
Chris also has a foot in industry, as a co-founder of Eigen Systems, an Australian startup with a mission to democratize access to quantum computing. Of course, we talked about why Bayesian stats are helpful in quantum physics research, and about the burning challenges in this line of research, but Chris is also a renowned author.
In addition to writing Bayesian Probability for Babies, he's the author of Quantum Physics for Babies and Quantum Bullshit, How to Ruin Your Life with Advice from Quantum Physics. So we ended up talking about science communication, science education, and a shocking revelation. about Ant-Man. A big thank you to one of my best patrons, Stefan Lawrence, for recommending me an episode with Chris. This is Learning Asians Statistics, episode 99, recorded January 15, 2024.
Hello my dear Asians, I want to share an exciting webinar I have coming up on March 1st with Nathaniel Ford. fellow Pimc Cardiff and causal inference expert. In this modeling webinar, Nathaniel will explore the world of causal inference and how propensity scores can be a powerful tool. We will learn how to estimate propensity scores and use them to tackle selection bias in our analysis. If that sounds like fun, go to topmate.io slash Alex underscore and Dora to secure your seat.
And of course, if you're a patron of the show, you get bonuses. submitting questions in advance, early access to the recordings, etc. You are my favorite listeners after all. Okay, now back to the show. It's Ferry. Welcome to Learning Bayesian Statistics. Thanks for having me. Yeah, thanks a lot for taking the time. I'm personally super psyched to have you on.
And also, I know a lot of my patrons will be very happy to see you and hear you on the show because they have asked me for a little while now if that was possible to have you on the show and well apparently nothing is impossible in the baysan world so really thanks a lot for taking the time Chris and actually let's start by talking about what you're doing these days right how would you define the work you're doing nowadays? and what are the topics that you're particularly interested in.
Sure. Yeah. So I'm an associate professor at the University of Technology, Sydney. I'm also a co-founder of a tech startup company. And both of these kind of have transformed me, like at least hopefully temporarily into more of a manager than a researcher. So the business is developing small, affordable desktop quantum emulators, trying to kind of beef up, enhance, enable new forms of teaching in quantum programming, which doesn't really exist.
And as a professor, I supervise a handful of graduate students postdocs. I made the mistake, maybe this is like advice for early career researchers, of allowing them all to select their own projects. So I'm supervising students who are all doing separate projects, all chosen by themselves. That means that they get to dive deep into their projects, but I kind of remain at the surface level. If I'd done it over again, I'd do it differently with maybe.
fewer students and working on topics that really interest me. But unfortunately, that doesn't usually generate much funding because I'm interested in the foundations of quantum physics, and that's more metaphysics or you might even say philosophy. But it's not bad. I get to help young students advance their careers and learn about new interesting topics and there's always time in the future to eventually settle down. Yeah, for sure. I didn't know you were also working on an EdTech company.
Yeah, you want to tell us a bit more about that? That sounds like fun. Well, I'm an elder millennial. I was born in the really early eighties, so that means I have to have side gigs. And yeah, it was something that we were interested in doing at the university quantum computing at the university. And what I realized was it's a very abstract thing. And it's usually taught from the context of physics and physics students are happy to just be, you know, do what they're told.
But computer science students are a little bit more challenging because they want to see something tangible and they want to build things and see the results of what they build. So we thought about building this kind of thing that they can interact with. And we made some prototypes and it worked really well in the context of teaching the teaching that I do. And we thought, well, and everyone we talked to in our field about this said that they wanted one too.
And then that kind of led us to the idea of starting a company. So we're at the stage of, of we have, we have customers, we've built prototypes, we have customers, uh, all around the world. And, uh, we'll make a big announcement actually at an event called quantum Australia and. that will, and then people can pre-order them, hopefully for shipping later this year. And it's, so the product is a small desktop quantum emulator.
Think about like the relationship between 3D printers that are in classrooms and commercial industrial scale 3D printers. So our small classroom thing is emulating the real thing. So, but it does everything that you need to do in the context of teaching. And it'll come with a full kit to teach quantum programming to hopefully eventually down to the high school and elementary school levels. Nice. Yeah, that's super cool.
And I am going to be honest that I don't think I can say I know anything about quantum computing. So why... Why would you like to do that? What are you, what do you think will that allow for a better education, basically, why would quantum computing help here? Well, when we make projections into the future, we see that we're going to need, the quantum industry will need lots of people, way more people than are in the pipeline now. So this addresses that market need really.
So the reason that we want to do it is to address that market need and do something that we think is best fit for it. Now as an individual, Why would you buy a desktop quantum emulator and learn about quantum programming? Well, you know, I think it appeals to the hobbyists in some sense. So if you're someone who buys new tech stuff on Kickstarter, then you, this is the sort of thing that you would buy because you're curious about it. Or maybe you just want to develop new skills.
Uh, eventually it will be a subject in, in high school that students can, can choose just like they can choose to do coding now in high school and programming. So quantum computing is something that is, it's a nascent field, but the 21st century will come to be known eventually as the quantum age, as quantum technologies develop. Okay. And what will that allow us to do? I think the only thing I know about quantum computing is that it's supposed to allow you to compute way faster.
So first of all, the idea I understand that well, And yeah, just can you give us maybe a rundown on quantum computing? Yeah. Well, it's not about speed. So there are some things that a quantum computer will be able to do that conventional, we call them classical computers, can't do. So the individual steps that occur within a quantum computer, carrying out an instruction is actually slower. It's the number are way fewer.
So the device itself is slow, which means that you wouldn't want to use it for simple things like adding numbers. Like there's not going to be a quantum calculator that calculates, that does addition faster. It's more obscure mathematical problems that people have connected to real world things like applications in cryptography, in the simulation of chemistry, those sorts of things.
all boil down to these mathematical problems that are difficult to solve when you encode information digitally with ones and zeros, as you would necessarily have to do with your computer. If you encode those problems into numbers that have complex numbers and real numbers and negative numbers rather than ones and zeros, then you can carry out far fewer steps to solve your problem. And a quantum computer would naturally encode those numbers. and be able to carry out those steps.
So it's select problems that you would use this device for. It's not just, you know, it's not in the, it's not this in the faster in the sense that eventually we'll have like a iPhone quantum or something like that. It'll be a special purpose component of a larger computer. Just like your CPU outsources graphics calculations to the GPU, it will outsource some quantum physics calculations to the QPU in the future. Okay, yeah, yeah. Yeah, I see. Thanks. Much clearer now.
So, yeah, and I get at least the main point. So, of course, I've already started on tensions, but I have so many questions for you. One of my actually planned questions was that... You have a very original origin story because you claim and you wrote actually that quantum physics actually turned you into a Bajan. So tell us why and I'm also curious if there are any key moments that shifted your perspective. Right.
Yeah. So yeah, we've been talking about quantum physics and not Bayesian statistics. So it all started when I was a graduate student and I was interested in this field called quantum foundation. So it's kind of really trying to understand the deep underlying questions about quantum physics. The problem is if you dig deep enough, you find that quantum physics is just a framework built on top of probability theory.
You've probably heard of things like the uncertainty principle, things like that, or that quantum physics is a probabilistic theory. And if you look at all of the debates that happen at the fundamental level and the foundational level of the field, they have more to do with the interpretation of probability than they have to do with physics. So when I was a graduate student, I thought, well, I mean, I'm not going to be able to answer these questions until I understand probability.
And I suppose in this... podcast, I'm preaching to the choir, but I came out on the other side of that as a Bayesian. Bayesian, I would put in sort of scare quotes because I think nowadays you can follow the recipes in a book that uses priors and Bayes' rule and it has the title Bayes on it without the need to actually have an interpretation of probability at all.
So it was more like in order to answer these questions and have a satisfactory understanding of what's going on in quantum physics, You need to have an interpretation of probability. Um, for most physicists, it's just an implied interpretation that they don't really think about. But for me, it, you know, it's, it came out with a subjective interpretation and that really helped me understand it.
Uh, but then I think at some point I was talking to my thesis committee and they didn't like this at all. And so most physicists, especially quantum ones, think probabilities are objective. So they told me to do something practical.
So I transitioned and then tried to start to apply Bayesian statistics to, you know, problems in quantum and quantum physics, which yeah, they're, it's essentially just classical statistics with unfamiliar models and different loss functions and you know, complex numbers are involved in some sense. Um, but yeah, it's basically just a way to, to derive a likelihood function.
Now, once you have a likelihood function, then you're just doing classical statistics, it's just a weird likelihood function. Um, so I was able to apply Bayesian statistics to problems in quantum physics. Um, so it was like, I started from this sort of philosophical point of view and then was told to do something practical. And so then I was able to. some practical things in applying Bayesian statistics to quantum physics problems. Did that change the view that your supervisors had?
I think to some extent it did. Those techniques and tools that we developed that they're being used in the field, although it's still dominated with frequentist methods. Yeah, interesting. Yeah. In my experience, that's the same. So usually people I talk to came to Bass through practical concern.
You know, like for instance, a PhD student who was completely blocked on her paper with the classic framework and then she just tried Bass because while it was... of her last resort and it solved all of her problems and now she's just doing that. But that's a very practical motivation. And yeah, I see most people coming from that angle. You're actually more in the outlier side where you've been more interested in the epistemological point of view and then shifted to actually doing it.
And yeah, actually what I've It's actually useful. Just show them. And then they'll be like, yeah, that does look good. And that does solve the problem we were having. So why not try that? So in my experience, that's been the same, too. And I'm curious, when was that work you did on practical Bayesian inference? When did you do that? Oh, that's gotta be 16. Yeah. 12, 16 years ago.
And we, so it kind of culminated in, we built this tool, we call it Qinfer, and it's basically a sequential Monte Carlo integrator that just naturally was able to solve the kinds of problems that people have in quantum Because it's quite difficult actually to use standard tools. Often they don't play nice with complex numbers and things like that. Don't naturally have the kind of loss functions and things that we use in quantum physics, kind of matrix manipulations that we have to do.
And at the time there wasn't that many, right? Computation-based statistics is a relatively new thing. There was a few tools, but not many. And so we ended up building our own and it's been used many times over the years. And that was maybe 10 years ago. I stepped back from that and handed it off to the next graduate student. Yeah, that's why I asked you, when did you do that? Because just a few years ago, there wasn't a lot of tools to do that.
So yeah, like you had, I'm guessing you had to write the algorithm from, from top to finish on your own. Yeah. And honestly, sometimes that's, that's better to do it that way. I mean, if you want to really deeply understand something, you have to build it yourself. You know, we can't build everything from scratch.
I mean, if, if you want to understand particle physics, you can't go build your own particle collider, but, uh, for things that you, you have the capacity to build, I would always recommend building it yourself or at least attempt to, and then realize what all of the problems, uh, are going to be if you wanted to make a really slick product. So get it to the point where you've built a prototype and then you really kind of deep start to deeply understand.
what's going on because a lot of times, especially with really usable products, they're really slick and they're just black boxes. And yeah, you can push the buttons and use them, but you don't end up developing a deep understanding of, of what's going on. Yeah, yeah, for sure. Even though hopefully if you had to do that today, that would be easier. You could use building blocks instead of really just starting from scratch. And thankfully- Well, I mean, an example is I...
Yeah, I can give you an example. So I have a student, an undergraduate student that I suggested trying a new it's jargon, but I'm sure people have heard about it. Maybe you heard about it. The Stein variational gradient descent method, which is a deterministic integration method and, you know, it's built into, um, Pi MC.
Uh, so I, the student can go and can go and try that, although it is quite, it's still quite difficult for them to build, build the quantum mechanical models that they have to build. So first I have them do it from scratch. And, uh, of course it It works to some extent, but it's not very efficient. There are a lot of things that tricks that come up in numerics. Like, what do you do if you're trying to take a logarithm and there's something close to zero, right?
Then you don't want them to have to figure out all those things. Have them build it first and then go. Yeah, yeah. Yeah, basically using... Yeah, I like that. Basically using a version from scratch that's... Simplified and then when you need to go industrialize that, well, just use the tools you have already on the shelf and maybe customize them if need. That's the beauty of.mc where you building blocks basically that you can personalize into your own Lego construction in a way. Yeah, for sure.
But that's awesome. Well done on doing that thing. And were you already using Python at the time, 16 years ago, when you were doing your own SMC or was it something else? No, the first version was built in Matlab, but as you might anticipate, we ran into license issues when we ended up using every one of the entire university's global optimization toolbox licenses. And so then we thought, well, this is silly. So then we moved over to Python.
The first one, yeah, it was kind of like the transition. So we had an early version built in 2.7, and then we moved to 3. Nice. Yeah. That's really fun. Yeah, in SMC, I know there are also some, like you can do that here with PMC now. So yeah, if one of your students is interested, They can contact me and I'll direct them to the persons who like doing that on the, on the PIMC community.
And, and you personally, do you have any specific instances to share or insights that you gained by adopting a Bayesian approach in your, in your research? I mean, it's hard to know, I suppose. I mean, I haven't given it a lot of thought, right? Because it wasn't like I had this problem and classical techniques weren't working for me. And then I switched over and found, you know, a particular set of Bayesian techniques that ended up working.
I recommend it to people because a lot of times, especially when you're thinking about things deeply and foundationally, like... You know, what are these things mean in quantum physics? Um, it, I always go back to simple classical examples and say, if you can understand this, or I guess it's a more negative thing, like if you can't understand this, then you're not going to even have a chance at understanding the more complicated thing.
So, you know, I go back to coin tosses and I say, okay, what does it mean in the context of a coin toss? And if you don't understand it there, you're not going to understand that quantum version of it. And the, yeah, the subjective interpretation of of probability just makes things more natural. I mean, it gives you a framework for thinking about things that you can always build on rather than the classical approach, which it doesn't give you that framework at all.
It's just grasping at straws and saying, okay, you know, what recipes work in this situation? And there isn't one coherent framework sitting behind it. Whereas the subjective interpretation gives you that.
And so you might not, yeah, you might not It's not like it gives you a specific set of tools that you can apply in every situation, but it gives you that footing, that foundation that you can build upon and always have that level of comfort, philosophical comfort saying, I understand, I know what's going on. Yeah, for sure. And to build on that question, do you have a favorite study or paper of yours where you used some Bayesian stuff at one point?
I'm curious to see, and I'm guessing listeners too, curious to see where Bayesian stats is useful when you do research in quantum physics. Yeah, there's lots of papers. I think most of them would be readable for someone coming from Bayesian statistics without knowledge of quantum physics. Because again, I try to frame it in this way where the quantum physics, the only point of the quantum physics is to arrive at the likelihood function.
And once you have that, then you can just do all the things that you're used to doing. Is it because your likelihood functions are always extremely exotic? Yeah, so the standard simple quantum experiment would be about estimating the parameter in a multinomial distribution. So you can think of a quantum experiment as rolling a die and trying to estimate the probabilities for the faces of the die. Yeah, but...
The thing is we don't, um, the, we have like loss functions that, I mean, yeah, they, there's some, some major season things in there. And then the issue is like, we have these loss functions that aren't, aren't ever used in, in classical statistics. And so a lot of the results, uh, just don't apply.
So you, you know, you, you can, you can sometimes appeal to, uh, like the law of large numbers or, or some of these you know, these theorems, but they, strictly speaking, our models don't really adhere to those, the assumptions that go into those theorems. So not only do we have weird loss functions, that allowed probabilities for the faces of the die are constrained in a weird way that relates to a positivity of some matrix that sits down the pipeline. So it's, yeah.
So oftentimes you would, you would, if you did it naively, you would end up estimating, um, things that make probabilities negative, which obviously doesn't make sense. So, um, yeah, there's weird constraints. There's an atypical, um, statistical models and, and then the loss functions that we use are quite different.
So, but, you know, if, if you know enough statistics and, and can accept that there are different, you know, that the possibilities extend beyond what you're used to, then yeah, you can you can work with it. A lot of times the things that you'd naturally try don't work. But, you know, it is still just a classical statistical problem.
We there was there was one paper where we were trying to find another way to problem in parameter estimation in quantum physics is the parameter that you're trying to estimate is itself a matrix. So it's not a real value. It's not a real value vector. It's a complex value matrix. And that's the thing you're trying to estimate. So I don't know if you're doing density estimation, that sort of thing. It's similar to that.
But we wanted to find the Bayes estimator for a particular loss function that involves square roots of And if you assume that all the matrices are diagonal, then you're back to a classical statistical problem and you end up with this funny loss function for classical probabilities that's somewhat related to some loss functions that are used in learning theory.
And then we said, oh, well, people actually haven't found the Bayes estimator or, let's just say, the minimax estimator for that particular function. So our quantum result immediately implied a result just that was purely classical. And we, the papers titled the papers estimating the bias of a noisy coin. So it's, uh, this, this actually crops up in, in social, uh, some social studies. So if I, if I ask you, if you cheat on your taxes, you're going to say no. So how do they do the sampling?
What they do is they, they introduce some randomness. So they they'll say, okay. roll a die, if the die comes up one, say yes no matter what. And so that the person who says yes can always claim that the die came up one. And so they feel like they can be honest. But if that probability of people cheating is really low, then you might get only one or two people saying yes, but one in six times they were supposed to say yes anyway.
So if you just naively kind of did methods of moments or some linear inversion, you would come up with negative probabilities. So this is exactly a problem that's embedded in a quantum mechanical problem. And so sometimes there's some nice overlap there. Yeah, for sure. That sounds like fun. And for sure, if you can add these papers to the show notes, please do, because I'm pretty sure listeners are going to be happy to. to check those out.
I already put some cool links in the show notes for people, but definitely papers are always appreciated, so feel free to do that. This is a safe place where we can all share our love for academic papers. Great. Yeah, I should warn the listeners though. Yeah, a lot of them are, they're cavalier, like a typical physicist. So it's very... We often take a conceptual approach to these things. Okay, interesting. Well, I read it because it must be pretty different from a statistics paper.
I don't think I've ever read a quantum physics paper. So yeah, for sure. I think I'm going to start by your books though, your books for children. I'm embarrassed to say, I think I'm going to learn a lot from them. So I'm going to start by getting to walk my way up to your papers. Sounds much, much clearer.
And maybe before actually talking a bit more about quantum physics and what you do and also the work you do on your children's books, but also science communication in general, and I'd like to keep talking a bit more about Bayesian stats because I'm curious, I'm always curious when I talk to a practitioner like you and so someone who is not... by training a statistician, but someone who really uses Bayesian statistics for their area of expertise.
What do you see as the biggest pain points in the Bayesian workflow right now? I think, as I mentioned before, the software that is typically used off the shelf doesn't accommodate the quirks and things that come up in quantum models. Some of them, they just won't accept complex numbers, for example. When I first attempted to use TensorFlow way back, TensorFlow 1, you couldn't even use complex numbers. to go back to the source code. And at that point, you might as well just build it yourself.
So yeah, complex numbers, matrix manipulations, we often have, as I said, lots of constraints. And when you attempt to use something out of the box, if it works at all, your whole screen is filled with warnings. And it isn't. It isn't as nice as the demos of the software. So I think for me, and possibly for people that are running models with lots of constraints, this is the biggest pain point at the moment. Obviously, the software will accommodate constraints, but it doesn't.
It doesn't seem to do so in a way that's natural and easy. Yeah. So ideally that like in an ideal world, that would be what you'd like to see to help adoption of patient training. Yeah. I mean, like a really concrete example would be, you know, I want to do sequential Monte Carlo on some simple estimates. I'm doing an experiment where I roll a die several times and I want to estimate the probabilities. It's of some biased die, but the probabilities come with a long list of linear constraints.
So not any probability will do. When you're doing the resampling, what is it that the software is doing to accommodate those constraints? approach is like, what doesn't really matter because there is no constraints. And so you can just throw a Gaussian on it and you know, it, nothing. Yeah, it's simple, but when you have these constraints, um, yeah, it makes, it makes things far, far more challenging. And sometimes the software just doesn't, doesn't accommodate those. Yeah, yeah, no, for sure.
I understand your pain. And I'd like to make your wish come true, but that's a hard one because in here, you're hitting a limitation, I would say, of the development process where you have to choose at some point if your package is going to be general or specific.
And packages like Stan, Climacy, TensorFlow, they have to be general because they are adopted by so many people with so many different backgrounds and so many different uses that we have to make choices that are going to work for most people and that are going to be optimal for most use cases. But that means for sure it's like If you're trying to accommodate everybody, nobody's going to be accommodated perfectly. Right.
So, yeah, like it seems to me like someone should go there and basically build a package on top of PIMC that just like addresses what you folks pain points are in quantum physics. Basically. I know there is such a package for astrophysicists. Of course, I don't remember the package name right now, but I'll try to remember and put that in the show notes. And I know that package built on top of times is really, really used a lot in the astrophysics field.
I'm not aware of any package like that in the quantum physics realm. But if any listeners do, but then please reach out to me and I'll pass that on to Chris. I'm sure his PhD students are going to be grateful. Yeah. Or if anybody wants to do that, get in contact with Chris, I'm sure he would have valuable points for you about what he'd like to see in particular. I think it's honestly there's a research question in there as well, right?
At least when we were doing it, that particular method that we were using, it was never applied or developed in the context of constraints. And so what you do when you're faced with constraints, at the time anyway, it was like sort of an open research question. So yeah, it's fair that... It's fair that the software just doesn't solve it for you because it may not be a there may not be an actual solution yet. Yeah, that's a good point also.
And so now I'd like to ask you a bit more about quantum physics per se, because, well, I'm always very curious about physics. So what in your line of research, what are the biggest questions, the biggest challenging you face currently? So we're at this weird transition point in the field of quantum technology where we can't in laboratories, university laboratories, build bigger devices.
So we kind of count the power of a quantum computer in the number of quantum bits or qubits that we can control. And nowadays it's very easy to get one qubit. was very difficult, but now there are many different modalities, trapping atoms, using states of light. All of these sorts of things can now be used to encode a single qubit, and that can be done in the standard physics lab.
Going beyond that becomes more difficult and you need much more funding to do it, but going much further beyond that is not a possibility within an academic. context. And so you need some large government organization or collaboration to do it, or you need industry to take over. So we're at that cusp where the largest devices are ones that are being developed by companies, companies like IBM, Google, startup companies like Rigetti, IonQ. There's a whole host of them now.
And what they're doing, obviously, secret now. So it's a weird place to be. I can't tell you, I can make guesses about where they are, what they're doing, what their problems are. But if they wanted my help, I'd have to sign an NDA, or they'd have to pay me and I wouldn't be able to tell you. So we've kind of transitioned into We're moving out of university research labs into government and company and multinational company R&D labs.
They have the same problems, but at a larger scale that university researchers had, which is just that to maintain the state of an isolated quantum system is very difficult. Any interaction. cosmic ray that comes in that you obviously can't control will degrade the information that's being encoded in these systems. And so they're very fragile.
We need to work out ways to provide better isolation, but complete isolation is not good either because you have to control them to carry out the instructions that you want. So it's kind of this Catch-22 where you want it to be completely isolated from everything except for when you want to actually. go in there and manipulate it in some way. So yeah, these are the problems. And I think theoretically there's still that big question about can it even be done? Can we even build a quantum computer?
There doesn't seem to be a reason why. If it turns out that we couldn't, we'd learn a lot about the nature of reality and the reason for why that's the case. But I think have the potential to be answered in my lifetime. Can we build a large scale fault tolerant error corrected quantum computer that carries out some calculation that would have been impossible to carry out with digital electronics? Yeah, yeah, that's pretty fascinating. And I'm really impressed by the depth and the width.
of topics in the research of physics. It's just incredible. I would refer to listeners to episode 93 that I did at CERN, the summer, I mean, 2023 summer, where we went deep on what do they do at CERN, what type of research, what does that mean, why even do that. And you'll see, well, some, you know, cross topics with what Chris is talking about, but also things that are completely different. And that's just incredible to see how wide these fields are.
And that sounds to me that's pretty incredible because in the end, that's just, you know, trying to understand the universe. So it's kind of doing the same thing, but it brings you... to directions that are completely, completely different. And that's really the funny, one of the fascinating things, I think, of these topics. And of course, go to the video version of the episode 93. You have the audio version if you have, but that was a video documentary inside CERN.
So I highly recommend checking out the YouTube link that I will put in the show notes. And actually, I'm curious, Chris, about also because now, as you were saying, you kind of have a management role, which implies thinking a lot about the future. So I'm wondering, where do you see the field of quantum mechanics headed in the next decade? Also, maybe how do you see patient stats still helping in this endeavor? That's a good question.
I think much like astronomy, for example, Bayesian techniques will see a wider adoption because at the moment, the way that a laboratory quantum physics experiment happens is really foreign to someone who does machine learning or data science where you have some data set and then you need to analyze it. No, what they do in labs in physics departments is if the data isn't what you wanted, then you just throw it out and start again. And, and you work until you have like really clean data sets.
So all of the all of the problems with data sets and things like that don't happen in physics labs. The physicists want to see the answer in their data. So the really sort of data scarce regime is unacceptable to them. They need to see it on an oscilloscope or something. The probability distributions essentially have to be delta functions for them before they accept that the experiment actually worked. But that's because we're doing really small-scale experiments.
Once those experiments grow and become large, we won't be able to do that anymore. If an experiment takes a week to run, You're not going to say, do it over again until you see a nicer data. You're just going to have to accept that that's the data set and you have to, you know, get as much information out of it as possible. And that's going to require utilizing the assumptions that you're making. In a sensible way, which will lead you to sort of Bayesian techniques.
So I think we will see wider and wider adoption within the quantum research fields. of Bayesian techniques going into the future, much like we have in the last two decades in astronomy. Hmm. Yeah. Uh-oh. Yeah, fascinating and... I really hope that these big questions you were talking about are going to be answered, at least some of them, because I'm just so curious about that. That would be just fascinating to have some of these answers at least come our way in the coming years.
um, relativity in quantum physics and how you can merge that. And so that's definitely would be incredible to at least understand that a bit better. And also, and I'm also fascinated by the fact that how do you do the experiments on this realm for now is just super complicated. Yeah, I think those are huge questions. I don't even think we've really formulated the questions correctly. I mean, that's my take on it. We have a theory that works really well at the moment.
In every regime we can test, our current best model quantum field theory works incredibly well. It's places that we don't even understand like inside the event horizon of a black hole. in principle, we can't even go there to get the data that we would need to find out if the theory works there. There's various takes on it. It's just a pessimistic take, which is like, maybe we've hit the limits of what we can understand given our capabilities in the universe.
And then, yeah, a more positive view is like, well, eventually someone will come up with some idea there was something that nobody could have seen coming. That's typically how paradigm shifts have worked in the past. So there's no reason to think pessimistically that will stop. But who knows, it might be the case. Yeah. I mean, I do hope for the second option, but you can never know.
And actually now I love the fact that you do a lot of science communication, of course it's also a job of these podcasts, so it's always something I'm very happy to talk about and I'm wondering if there are some common misconceptions you've seen about quantum physics, maybe even about Oh, yeah. Well, I wrote an entire book for, not for children. It's, yeah, you may have to edit this part out because the book's called Quantum Bullshit. I don't know if that's allowed in the podcast.
I'm French, so we have no worries with swear words. Yeah, in Australia it's similar. Yeah, so that's the title of the book. The subtitle is kind of a science comedy. So the subtitle is How to Ruin Your Life with advice from quantum physics. And it kind of goes through a lot of the common misconceptions and how each of these major concepts in quantum physics are misused.
Things like superposition, entanglement, quantum energy, quantum uncertainty, these sorts of things, how they typically are misused. And yeah, what's the most sensible kind of way to understand them without having the mathematical background that underpins the framework of the theory? So yeah, there's lots of them. And if you want the comprehensive list, definitely check out the book. I'll give you like a typical means things can be in two places at once.
And that just like, just saying it out loud should make it clear that that's a logical contradiction. Because, you know, a dichotomy between true and false, and you can't have something that's both true and false. So sort of a logical contradiction. But that being said, you still, you know, physicists will still say things that sound kind of like that.
So an example might be this famous double slit experiment where you have some sort of screen, it has two holes in it, and you fire electrons at it and you see an interference pattern on the other side instead of just two dots where the electrons landed, suggesting that the particles interfere with each other. And if you do it one particle at a time, that means it has to interfere with itself. which means it had to have gone through both slits at the same time.
So the electron had, or whatever particle it is, had to be in both of those places at the same time. But we always run into these problems when we try to explain what's going on in quantum physics by analogy to our everyday world. It's just a different world that we don't have access to. We don't have a language and a familiarity with. So we have to use these analogies. But... you know, they very quickly break down. So that's absolutely not what's happening.
Uh, and things can't be in two places at once. And yeah, you shouldn't, uh, you should buy a quantum crystal or something because it promises that, that it can do that. And for the Bayesian, I find actually, um, uh, yeah.
So, you know, when you You can kind of explain to people the way I do it now is to walk through that idea that in quantum physics we have these concepts and we have to use a language that we're familiar with but that language isn't really suited for trying to do anything beyond explain that one special thing. You can't extrapolate using those analogies because you'll quickly fall prey to misconceptions. So That's typically how I explain it in the context of quantum physics.
And quantum physics is actually quite popular in the popular culture. I don't find that Bayesian probability is so popular in popular culture. So, you know, the word quantum crops up all the time, attached to things. Nobody's selling Bayesian healing crystals. So, these aren't like popular. Oh, that's actually not a bad idea.
Yeah. But so you don't need to approach it the same way because you're not typically talking to a lay audience when you're talking about misconceptions and Bayesian probability. Usually it's someone technically minded who knows something about some technical topic that the probability is being applied to or probability itself. In physics, the main problem that people have, you could call it a misconception, is that Bayesian methods are subjective, whereas frequentist methods are objective.
And as a scientist, you need to strive for objectivity. So that means that you shouldn't use Bayesian methods and you have to use frequentist methods. But the easy thing to point out is to... What you could do is just... have them walk through how they would apply frequentist methods and then point out that they had options and then they made their subjective judgments on which options they were going to choose to solve their problem. So it's no less subjective.
And in some sense, it's worse in the sense that you're not being honest about the biases that are going into what you're doing. So yes, Bayesian methods are absolutely subjective, but they're subjective in the most honest way possible. Yeah, that's usually the way I go about it also.
The faster you're going to abandon the idea that there is an objective way of seeing reality, at least the way we are made, you know, if you're homo sapiens, the faster you'll be able to think about real ways to actually try to understand what's going on. And so, yeah. It's usually the way I go about it. But yeah, I mean, these are fascinating topics. I, we've actually covered some of them in some of the episodes we've already done on the show.
So the one, one before you was episode 97 with Alien Downey where he actually talked about that where. He has also a blog post about it comparing this idea that Bayesian results converge to the frequentist results to the limit. And so that was interesting to talk about it with him because he actually argues that it's never the same. And that's not a problem. You should still choose the Bayesian framework, actually. But that was interesting.
So you have that for people interested and also I'll put in the show notes. So I'll put that one and I'll put in the show notes, episode 50 and 51. 50 was with Aubrey Clayton, who wrote an amazing book called Bernoulli's Fantasy and the Crisis of Modern Science. So that's more about the history of statistics and how basically, how and why came to dominate the scientific world. So much more epistemological, very, very fascinating book.
And episode 51 with Sir, only Sir we've had on the podcast, I think, Sir David Spiegelhalter about risk communication, how to talk about risk, especially to a lay audience. and people who are not educated in stats or in the scientific method. And that was, that was way closer to the COVID pandemic. So that was very interesting to talk about that with him, because these topics were absolutely important in time of pandemic or very stressful situations. Right. Who would think so, right?
That the nerds actually had tried all along to talk about stats and probabilities. This can save you during a pandemic. But yeah, I mean, this is also something that I think must be added in these discussions. Often, it's not really in the papers that you see these misconceptions, but it's more in the way the papers are interpreted by people who are not equipped to read the papers.
And so I think there is a... a job in the world that needs to be filled, which is basically making the bridge between scientific papers and then what ends up in the newspapers. And that is a bridge that still has to be built. And we're trying to do that in a way with our work, but it's still so much things to do still. Sometimes my game is really to do that. It's trying to see what people are talking about on Instagram or stuff like that.
And then actually try and go to the source that they are supposed to quote, you know, to site. And then you see that basically it's just like the first person who reported on the paper did understand the paper or just read the abstract and the title. And then just everybody cite that first source. So basically the first error is just like trickled down and that's just fascinating. Yeah. Yeah, I think the solution has to sort of include actually that people write fewer papers.
I mean, there's over a million academic journal articles published every year, and that's more than we can read, right? But there's the perverse incentives in academia now that kind of force you to do this, which means also that like most of those papers shouldn't have been written, I think it would be better if we had a more careful approach where the result is fewer papers that are better written. Yeah, that could have been more.
And also it's something we've talked about on the podcast several times, incentives in academia. It's hard to change, but needs to be changed. But yeah, hopefully that will... And having people like you in academia definitely helps. Well, hopefully with time, it's going to evolve. But yeah, and we could continue on that road, but it's going to be a three-hours episode, and I don't want to take too much time to you.
And actually, that's a very, it's the very first episode that we do where we are actually time traveling, right? Because it's still January 15 for me. at night and it is January 16th in the morning for Chris. So thank you for calling from the future, Chris. We solved the glass problem. The sun rises tomorrow. Yeah, I can tell you that. Yeah, I can see for now, no apocalypse. So that's cool. Glad about that.
Yeah, I had other things to add about your very good points about communication and so on. But of course I... I think I forgot about them. I will just refer people to the show notes. I'm gonna put the episodes I mentioned in there. And oh yeah, one thing, I tracked down the Python package I was talking about for Astrophysics. So the package is actually called Exoplanet. And yeah, it's a package that's built on top of PymC.
to do probabilistic modeling of time series data in astronomy with a focus on observations of exoplanets. So I put the notes, the link already in the show notes, and that's developed mainly by Dan, Ferm, and Mackey. So people who are working on that definitely take a look at a very cool package, very well maintained. So Chris. I've already taken a lot of time from you, but I'm curious. I want to talk a bit about your children's book.
Of course, you've written about quantum physics, about general relativity. Patient statistics also, you've written a book, I think, about that. First, I'm definitely going to buy those books if one day I have kids. That's for sure. I'm not going to read them stories about... crystals and things like that, much more about that kind of thing.
No, first, keening aside that I think that's a very good service you're making because definitely there is a big lack of scientific culture, I would say in general, in the audience, just understanding probability. The main thing I have to face is often things like Well, you said that thing would happen with a 30% chance. It didn't happen. Hence the model was wrong. And that's just like, this is kind of the, this part of the misconceptions on, on the part of, this is the burden of a statistician.
But I think it's extremely important to make people more aware of the scientific methods, more scientific savvy. First pick is way more interesting than what pop culture makes it look like. You know, you don't have to be crazy. You don't have to wear a white coat. You don't have to be a genius to understand science. And you don't have to be a genius to use science. So, yeah, I think it's extremely important what you're doing.
And mainly to go to my question, how, how do you approach simply simplifying such complex topics for young minds and yeah, how do you think about the way you teach that? Yeah, that's a good question. I think you hit on a lot of good points. And there's a lot of obvious traps that people fall into, right? That you might think, well, science is boring, so we need to spice it up. This happens all the time.
If you see scientists on daytime television or whatever, they inevitably do some chemistry experiment where there's some explosion and gives people a really distorted view of what science is. Not only is it... People think that it's old white dudes in lab coats and geniuses, but also people have this misconception that it's all about excitement and explosions and chemical reactions and cosmic awesomeness.
But science is at its core, this fundamental framework for navigating the world in the... most sensible way possible. So when I approach the children's books, I try to really simplify not only the concepts, but just that overall sense of what I'm trying to do. I'm not trying to create some extrapolated vision, some way too exciting picture of what science is.
What I try to do is I try to give examples, analogies, categories, kind of abstract things that give people some comfort, some tools that they can use to try to understand or appreciate what's happening in these fields. it becomes obvious that the books are for parents, not necessarily for babies. Um, and I think a lot of the feedback that I get is from parents who say things like, Oh, I wish I had learned this topic in school in this way. Right.
Uh, and you know, it all boils down to this, this notion that when we learn things, what, what we're doing is just building up our repertoire of of analogies that we can use to understand them. And the more that you have, the better, right? And the sooner you start, the better. I think there is a misconception that there's one unique special way to understand a concept. And if it's only told to you in that way, some light bulb moment will happen in which you all of a sudden understand it.
But that's just not you at some point in the future, you say, Oh, I feel like I understand that. But there wasn't a, there wasn't a turning point. There wasn't a light bulb moment. There wasn't a switch. It was just time and, and building up those, those analogies and examples that at some point you just feel comfortable and that's all there is to it. So it's actually surprisingly easy. It's a lot easier than people think.
Uh, you know, because the, the task that I set myself is, is not such a high bar, you know, just give a simple palatable analogy for some core concept in the thing that you're talking about that, that anyone can understand. Hmm. Mm hmm. Yeah. Um, yeah, definitely. It's. Again, extremely important, so thanks a lot for doing that.
And I do think that it's very important to make science more, look more human and write it more and more approachable because I often people see that as very dry endeavor, but I think actually counting stories. about science and scientists and normal scientists, right? Not the weird scientists from the movies is extremely important because that's also how we learn, right? We learn a lot. Our brain is like that. We love stories and we love learning through stories.
Like every equation you learned at school has actually a story behind it. Lots of people have worked on it. Lots of people have. failed and depressed because they couldn't find the solution. And thanks to their work, then afterwards it unblocked a lot of things that you can actually do now. Just knowing about relativity makes us able to be located through our phone. We can use GPS very accurately because we actually take into account relativity. Well, it's pretty incredible, right?
I'm guessing most people don't know that. So yeah, I think it's extremely important. And actually I've watched very recently a series, a Netflix series that does an extremely good job, I found illustrating science like that. So it's still of course romanticized a bit, but first the physics that's in the show is pretty good and... accurate, they don't refer to absolutely completely crazy theories because the series is called Lost in Space and the beaches unite.
Something happened on Earth, I'm not going to spoil it, but something happened on Earth and then some people had to go and try and colonize Alpha Centauri and we follow the adventures of the families who do that. The science is pretty good on that and also the depiction of the science is, I found, very interesting. We have some very interesting scenes where it's like, oh, that's magic. That's not magic. That's math. That was really cool.
I'm not going to spoil, but I definitely recommend this series. It's really well done. And of course, well, your book, Chris. And well, I think we could, we can call it a show, I think, because I've already taken a lot of time from you. And for people watching the video, you can see that the sun is setting down for me. So the, the luminosity is getting down. But I'd like, so before the last two questions, my last question would be a bit of a general one. If you have any.
advice, Chris, for students or young researchers interested in quantum physics or even patient statistics, what advice would you give them to start in these fields? Yeah, I think for young people that have time on their hands, my advice is quite simple is to study mathematics. Mathematics is obviously the foundation of statistics, also the foundation of quantum physics and all of physics. I see students coming into university who are very excited about science.
They come in, they say, I've read all of Brian Greene's books and Stephen Hawking's books. I'm here to be a scientist. I live to be a quantum physicist. And then you hand them a test with only math problems on it. And they get very deflated because nobody told them that it was all about math. So it's the way that I came into the field. I was never really interested in physics or science. I was a math student.
And when I finished my degree, it was more about how am I going to apply my skills in solving math problems. And that served me very well. So yeah, become proficient at mathematics. There's lots of fun stuff in mathematics when you, you know, at the surface level, depending on the way it's taught can feel boring.
And, but yeah, the further you dig deep into it, the more interesting and more exciting it gets, and it will provide you with a deeper understanding of the field that you end up applying it to then. than you could have ever imagined and certainly more so than the people that are just still at that surface level. So yeah, that would be my advice. Also, especially for young people, for students, life is very long and now is the time that you're encouraged to make mistakes.
And it's really the only time in your life where you can make mistakes and get rapid feedback. And that's the thing that's encouraged and that's the best way to learn. So, you know, approach it from that perspective and also drag it out as long as you possibly can. Yeah. Completely agree with these recommendations. Learn math and learn it well and take risks very, very young and for the most time you can. Because yeah, that's definitely helpful.
Even financially, like a good financial advice, if you have to take risk and put all most of your money on stocks, that would be when you're young and then when you get older, you get a bit less, a bit more risk averse on your portfolio investment. Well, I would say that's the same thing for life and for rapid feedback and failure when you are young and not having your responsibilities to do that, you know, take the risks. And learn math. That's not a risk at all.
Awesome, Chris. Well, I'm going to let you go. But before that, I'm going to ask you the last two questions I gave a guest at the end of the show. First one, if you had unlimited time and resources, which problem would you try to solve? I think that's easy, at least in my discipline, I would build a large scale quantum computer and then I would set it on the task of simulating various materials until it found a high temperature or room temperature superconducting material.
And then we'd build that and go, have free energy around the world. That sounds nice. I love that. Yeah, awesome. You're the first one to answer that, but love it. And second question, if you could have dinner with any great scientific mind that alive or fictional, who would it be? Yeah, I mean, these sorts of questions I think are difficult, especially for someone with an analytical brain. You know, you've got the one, the devil on your shoulder saying, yeah, play along, it's a whimsical game.
I thought about this actually. So I think there'd be some inherent problems with obviously with a dead scientist.
You know, there's obvious problems, but I think the ones that people don't think about are Say, you know, I brought what I Guess this is a magical scenario, but I don't know if it's I go back in time or they come to our time But in some sense, it doesn't matter So I would prefer they come to our time because you know, if go far enough in the past and they don't even have toilets So let's bring them to our time, but there's a problem. Like if I brought Einstein here what what would I have to do?
Would I have to explain a century of advancements in like the actual field that he came up with? And would he even accept it? Like even in his lifetime, he refused to accept all of the consequences of quantum physics. So, you know, it actually wouldn't be a great conversation. I think scientists from the past would just be, it would be too difficult to communicate. magically overcome say some language barrier.
Like they're, yeah, the contributions they made obviously are timeless, but like that conversation that you could have wouldn't be very insightful. So I feel like you'd have to go with a living scientist, but then the problem with a living scientist is like, I can just email them if I had a specific question. So it seems like far more, far easier than... than organizing some dinner, which you can have when you go to conferences anyway.
So I've been to dinner with Nobel laureates and stuff and celebrity scientists, and one of them was probably enough. So then I think you're forced to go with a fictional character. I don't know how many of your guests pick a fictional character, but my favorite fictional character with a self-proclaimed great mind is Marvin. paranoid android from the Hitchhiker's Guide to the Galaxy. So I would uh, I'd have dinner with Marvin and I know exactly what I'd ask him to.
I'd ask him about AI alignment. Um, because I think it seems to, he seems to have been solved with Marvin and uh, I think he would just give a wonderfully nihilistic answer to what is AI alignment. Yeah. Yeah, no, that'd be fun. Yeah. I take part in this dinner. I don't know. Let me know when that happens. You want, oh, you want a bonus question, uh, physics related, a choice like that. We had to make, uh, last time we did a retreat at PIMC Labs, we do a retreat, uh, every year.
And, uh, of course, it's just a bunch of nerds getting together. So we always end up with, uh, very nerdy questions. And, um, yeah, this year, I think one of the main questions where So yeah, the year before, one of the main questions was who would win in a plane war, so in an airplane war, Earth or Jupiterians. And this year, but the one I want your input on is this year was, if you could choose between these three options, which one would you choose?
If you could know what's... like what it's like to be in the quantum realm? Or if you could go inside a black hole and know what's there? Or if you could go to an alien planet and meet them and talk with them, what would you choose? Right. Uh, there's only one, there's only one correct choice. It's the third one because the other, the other two, uh, would be bad. bad decisions. So it's the alien planet, yeah. There is no quantum realm. I wrote a blog post about that.
I'll give you the link for the listeners. Oh, perfect. So you can't go there, obviously. There's technical challenges clearly with shrinking a human, but also, yeah, our entire sense of perception is built on our mesoscopic relationship with the world. Like clearly there'd be no sound, there'd be no notion of sight. So even if you could get around this weird idea of shrinking yourself, it wouldn't be a place to experience.
And then inside a black hole, every direction points down and you'd be spaghettified. So it's a bad idea. That'd be a problem. Yeah. I mean, I love that statement. So let's go to the alien planet. That's a technical term, actually. Yeah, yeah, yeah. Spaghettification. Yeah, yeah, yeah. And yeah, I mean, I'm shocked by the revelation you just made on this podcast that Ant-Man is not a documentary. That's just, I'm just shocked. So I think it's time to stop the podcast.
First of all, because I don't have any more light and second, because well, I've taken a lot of time from you. Thanks a lot, Chris. That was really awesome. I learned a lot and we covered a lot of topics so that was really perfect. As usual, I put resources and a link to our website in the show notes for those who want to dig deeper. Thank you again, Chris, for taking the time and being on this show.