This week we sit down for a conversation with Blake Johnson and Robert Sutor from IBM's quantum computing ecosystem. We discuss the technology's implications on drug discovery, security, and cryptocurrencies. Plus, tips on learning the basics and running your own experiments with the hardware you have at home.
Blake has a PhD in physics from Yale and is the quantum platform lead. You can find him on Twitter here and read some of his recent writing here.
Robert is VP of IBM Quantum Ecosystem Development, IBM Research. He's the author of Dancing with Qubits and has put together a great list of tutorial videos on his website.
No Lifeboat badge winner today, but if you're a fan of Schrödinger's cat, be sure to check out this question from our Quantum Computing Stack Exchange.
Bob Suter: We just passed 700 billion circuits being run on our hardware. So this isn't day two or week two or even year two, you know, we've had over a quarter of a million people registered to use the systems online. So this is something that many, many developers, thousands of developers have already had hands on experience with it.
Ben Popper: Are you ready to start writing your tech story? Join an Ironhack bootcamp and learn the skills you need to pursue a meaningful career in tech. Visit ironhack.com/write-your-story to find out more. Let's write your story.
Hi everybody. Good morning. Welcome to the Stack Overflow Podcast. This is Ben Popper Director of Content here at Stack Overflow, and I have two great guests with me today.
Blake Johnson and Bob Suter from IBM. And we're going to talk about something I understand not at all, which is quantum computing. Hi everybody. Hi Blake. Hi Bob.
Blake Johnson: Hi, how's it going there?
Ben Popper: So I guess to start maybe each, tell me a little bit about what it is you do, you know, these days, what your position is and what your, you know, that involves.
And then maybe step it back a little, you know, how you got to that position. What's your sort of career trajectory in software has been like?
Blake Johnson: Okay. So I'm the, I'm the Quantum Platform Lead, at IBM Quantum. Uh, my team is responsible for building the platform, which users can run, uh, quantum programs on our, on our systems.
And we also build not just software, but the hardware that ends up turning a program that the user wants to execute on a quantum computer, into the signaling that sort of actuates the gates, the quantum logic on the chip itself.
Ben Popper: And so where we are right now, not too many organizations have access to an, a physical, you know, the actual quantum computer.
So this is people sending you a job, like bringing in their punch cards and running it, you know, if we were to make an analogy to the way it used to be. And then you send the output back to them after you've run it.
Blake Johnson: Yeah, that's right. Except for, instead of a bunch of users sharing one machine, we're fortunate to be able to have, uh, 20 or so machines, uh, online that the users are sort of sharing time on or in a cloud delivery of quantum computing model.
Bob Suter: And it isn't--this is Bob Suter--and it is automated. I mean, you, you can sit at your laptop, right? And, uh, you can do everything online. We have, what's called the IBM Quantum Experience. You can use Jupiter Notebooks, you can use drag and drop. So right from there, you can build your quantum circuits and can do whatever work you want under the covers.
It goes to the quantum computers and comes back. So there's no physical handing off to another person. It's a cloud based service.
Ben Popper: And so what kind of jobs and projects are people sending to you? What do you see these days when we're still, I would say at the early stages of quantum computing becoming an accessible industry and accessible technology?
Blake Johnson: I mean, people are doing lots of, of things. I mean, one of the things we were talking about in our recent announcement of our, of our software roadmap, our developer roadmap is, is the different kinds of users that are coming in and trying out the service from people that are just learning like an education audience of, you know, just learning, what is quantum computing, what is quantum mechanics? But then you have people that are sort of developing, trying to develop new capabilities, right? And you have people at a low level of access that want to, uh, manipulate the, the direct signal and we call them 'colonel developers'.
You know, that that really want to get into the details of how do you build a high quality circuit, including how do you build the, the constituent elements of it, like the quantum Gates and be able to manipulate very, very low level details. You have users that don't want to get quite so far down in the weeds, but want to build, you know, algorithms.
They want to be able to do sort of mathematical or other algorithmic tasks with those machines. And so those are more sort of computer science, your sort of traditional computer science audience. And then you have, uh, people that have corresponded than like take those algorithms and apply them to solving real problems.
We think of them as this kind of model developers, because a lot of the early, early use cases we have in mind have sort of a scientific or mathematical bent to them still. Um, but there are subject matter experts in optimization, chemistry, material science, and so on. They're trying to take those algorithms and, and actually put them into practice at least today on sort of small toy problems.
Ben Popper: So for folks who don't know, when you're talking about building a quantum circuit, what does that mean? What are people trying to either learn there or do there? And what step in the process of, you know, running a program is that?
Bob Suter: So let's, let's fix this idea of what a quantum circuit is. So when you construct a sequence of instructions, which we call gates, just like you do in the classical sense, going way back to logical circuits and gates, you put them together and, and you create a circuit and these are quantum instructions. You then send it from, let's say your laptop, your desktop, or it could even be from a container on the cloud, uh, across the cloud gets in the queue and eventually runs on the quantum computer.
You can do that manually, you can construct those circuits piece by piece, or you can use higher level parts of the library that will construct the circuits for you. But by the way, I just want to, since now that we've defined circuits, um, I want to give you an idea of, of just how far down the line we are.
We put our first quantum computer on the cloud in may of 2016 and we just passed 700 billion circuits being run on our hardware. So yeah, this isn't day two or week two or even year two. But, um, you know, we've had over a quarter of a million people registered to use the systems online. So this is something that many, many developers, thousands of developers have already had hands-on experience with.
Ben Popper: I know just from personal experience. 'Cause they're our clients sometimes for Stack Overflow that there's a company called Strangeworks that also offers like a platform like this, you know, come in, use language of your choice, send us your stuff through the cloud. We'll spit it back to you. What are the other players, uh, you know, in this space who are offering sort of, yeah, quantum as a service through the cloud, where folks can come to you with instructions or ideas. They send it to you, you run it and then send it back. Who else is playing in that space?
Blake Johnson: Uh, so there are a few players that are in some ways, aggregators of other quantum hardware, right? Others sort of big name, uh, cloud computing companies.
We're a bit unique at IBM in, in sort of, uh, having the end to end solution of developing our own quantum hardware and being able to deliver it to you in a sort of a cloud experience, uh, online. The example you gave is Strangeworks, right there. They're just to kind of in the cloud and user facing side of it, without being a provider of, you know, they don't build their own quantum hardware. They, they experiment in the space of how to deliver quantum hardware from others in ways that users that are easier to consume for developers.
Ben Popper: Right. So for people who don't know, can you tell me a little bit about the quantum hardware you develop? What goes into the hardware? You know, how it can be done safely, efficiently, and, uh, I guess with, yeah, a certain amount of, um, redundancy or reliability?
Blake Johnson: I'm curious about what you mean by safely in that sentence?
Ben Popper: I guess. I don't know what goes on in a quantum computer, but, in my mind, something could go wrong and it would be sucked into a worm hole.. Like Antman trapped in the
Phantom realm.
Bob Suter: No black holes, no worm holes.
Blake Johnson: No black holes being created.
Ben Popper: This isn't like the Hadron Super Collider, there's no-- okay.
Bob Suter: If I could insert something here, for any of you who watched His Dark Materials on HBO, they had a computer which they call The Cave. Right. And it looked eerily similar to the insides of, of our quantum computers and others too. So it's very funny that in some sense, people, you know, science fiction had talked about quantum computing for many years now we're building quantum computers and now we're influencing science fiction, right? To look like what we're already doing, But we don't do anything like they do in that, that show, just to be clear.
Blake Johnson: Uh, but to get to your question, I mean, we make, uh, our quantum hardware is based off of superconducting qubits. Um, something known as a transmon. And, uh, I mean, because it's a, uh, superconducting material that we use to store that the zeros and ones, the logical zero in ones that if you have your quantum bits, we need to cool those things down to very low temperatures.
And so like these beautiful images you see of like the thing that Bob was just describing the so-called chandelier or a dilution refrigerator, uh, is the kind of apparatus that cools it down to those very low temperatures. And one of the remarkable things is that that technology itself was a science experiment in the 1980s.
And is now kind of a turnkey technology that you can, you can buy and use that as, as a kind of the plumbing or infrastructure to, to, to build up a quantum computer.
Ben Popper: And so the hardware is part of it. The software is another part. If you were talking to somebody who was curious about this stuff, they might be, you know, a software engineer for a few years.
What kind of skills or programming languages education would be useful to youas you know, know, if you wanted to get into this, is there a good grounding, um, you know, certain languages or, um, techniques, certain training that would help you as you decide to try out quantum computing?
Bob Suter: Python, to begin with. We made a decision about five years ago that we would build libraries on top of Python rather than create an entirely new programming language for anyone who's been in the industry for awhile.
You know, it's great fun to create brand new languages. But then they forget, you have to train thousands of developers and you end up supporting the language more instead of what you were trying to create the language for in the first place. So we decided to build on Python because, you know, at the time we looked and there were 8 million Python developers, I mean, your, your users are very well aware of that.
It also allows optimization underneath the covers. So even though you're using Python, you can write C routines and things like that. So that's where you would start. And then on top of that, is is Qiskit which is an Apache licensed open source software development kit. And maybe Blake, you could talk a little bit about the layers of Qiskit for, uh, the different types of developers.
Blake Johnson: Sure. I mean, so at its core is, is a, is the kind of compiler, which is, that's kind of the core of Qiskit of on GitHub Repo, it's called Qiskit Tara but we think of that as, as being really the core of Qiskit, it's about doing all the transformations to efficiently optimize circuits so that they can run on, on real hardware.
And that's qubits because, uh, even at the level of circuits, which feels fairly low level, it's still useful to be able to talk in terms of mathematical distractions. Like I want to be able to create an interaction between these two qubits the gate between those might not exist in physical hardware.
So we have to rewrite your circuit into the actual Gates that are available on the chip itself. And so like, all those transformations are part of the Qiskit, of the core of Qiskit Tara. But then we have tools for like applications development and there's new application modules, like the optimization module there's a chemistry module. Very very soon, there'll be some additional modules for, uh, that take our first steps in sort of machine learning and, and in finance. So.
Ben Popper: Neat. Okay. Well, that's a good segue. Cause I had a few questions that kind of come out of some of my other interests. One thing I heard early on was that, you know, quantum computing, if, and when it becomes, you know, extremely effective, if you get closer to that, quantum supremacy or whatever it may be, would have a really strong impact on a lot of what we consider to be best practices and cryptography and security today that you'd be able to brute force your way through things much faster.
And that would, you know, need the security industry would need to adapt to that. Yeah. What's your take on that? Is that something that you feel, is that a valid concern or a valid, uh, idea that it might disrupt that industry?
Bob Suter: Well, it's security is always a great topic, but it's also a topic where there can be a lot of hype that you have to read the details, you know, it's good tobe healthily nervous about cyber security. A lot of this goes back to the mid 1990s when Peter Shor, who has been a mathematician at Bell Labs, uh, created a quantum algorithm for factoring very large numbers. Now most people don't factor numbers once they get out of high school, frankly.
But, uh, if you're into mathematics or things, you care about that. But it's also the case that several of the common cryptographic protocols depend on your not being able to factor some very large numbers because the keys are related to, to the factors. So the idea is that if you could factor such a big number, you could get in there and decipher whatever the information is and so forth. Now, the reason why it's a bit of hype is because you would require tens or hundreds of thousands, or even millions of qubits to do this. Now we have the most aggressive program in the industry. We're going to break a hundred qubits this year, 400 next year. So here we're talking from going literally double digits, triple digits to millions, or even tens of millions of qubits so it's a ways off given the way that people would imagine, right. But there's more, a little bit more of the story, but just to be careful, right. There are so-called quantum proof encryption protocols that are being standardized by NIST and people should start moving over to those because should it ever be the case that quantum computers can crack some of these current ones, well, you know, if you had moved to an, to, to something else, you'd be okay. So that's the direction it's part of your cyber security strategy and much less a part of quantum per se.
Ben Popper: Gotcha. And just you were mentioning that you have a program, you know, with an aggressive goal for growth from a 100 to 400 to a thousand.
Does quantum computing follow Moore's law in some way shape? Does it double every two years or, you know, you know, if that path was to follow, then it a million wouldn't be so far out.
Blake Johnson: So we have a different preferred way of counting the power of, of a quantum computer as its benchmark known as quantum volume.
Quantum volume in some ways is related to a mathematical concept of, of dimensionality of Hilbert space. But it's an crudely. It's basically the number of qubits or the two to the number of qubits that you can fully exploit in your quantum hardware. And so it has--at IBM we have a roadmap which, which predicts that we will double the quantum volume every year.
Um, lately we've been slightly ahead of pace, uh, against that roadmap, but you, you need. Very very large quantum volumes to get to the, the kinds of power that you would need for, for factoring a cryptographically relevant numbers.
Ben Popper: Alright. So let me run one more by you that I've heard a bunch, which is often in the news, cryptocurrency and Bitcoin, that, you know, the development of a powerful quantum computing could have a big impact on that industry.
Blake Johnson: Where a lot of work goes into, you know, crunching these hatches, stacking up a lot of GPU's that, you know, efficient quantum computers might be able to introduce into that economy. You know, something that is at a, at a completely different, you know, sort of step function in terms of its capabilities. What do you think about that?
So, I mean, If you take like Bitcoin as a classic example, right. I think it uses a SHA Hash has part of it's the primitive in terms of, or you have to, you have to --
Ben Popper: It's SHA-2, or have to do it twice, but yeah.
Blake Johnson: And you have to do it twice, and you're predicting the inputs that lead to a particular output, right. Is the, kind of the, the proof of work concept, that problem, I mean, making the, the, the computation by Hash and the quantum computer is a problem, which is almost as hard as factoring. So it's, it's, we're really not talking about near-term applications of quantum computing.
Ben Popper: Okay. So let me take a different tack. You mentioned a chemistry kit. What are some of the things that you are excited about that we might see in the next one to five years where quantum computing could be applied to a certain space, a certain problem, a certain industry, and you feel like, uh, you know, it might be transformational in some way?
Bob Suter: Let me just start a little bit with why chemistry. So quantum computing is based on quantum mechanics, which is an important part of physics that dates back really to the early 1900s. And when, when you think about quantum mechanics and what it describes, it's things like atoms, electrons, photons, molecular reactions.
Things that are very small. So if you're going to be computing with items in, in natural sciences that are ruled by quantum mechanics, it makes sense to use something like quantum computing, which is based on the same principles. So it's really kind of an apples to apples types of comparison. People have pointed out for decades that our current, you know, whether you want to call them binary or digital computers, we usually just call them classical computers.
There's no reason why they should work well with, with natural sciences. And in fact they don't that we can quickly swamp them with very small examples, such as from chemistry. The other couple of areas that people like to highlight one is AI. The reason is, is because if you look a lot at the AI techniques, when you go down deeply into them, they're all math.
So you ask the question, can quantum computing help you do the math faster? Right. So that's a good thing. Do the AI you're doing now, just do it faster. But the other thing is that quantum computing is a completely different model from classical computing. It's not just an improvement, it's just completely different.
So what you say, well, I have this, this different view of the world, this different way of computing things. Can I see things using quantum computing that just couldn't see very easily for AI for classical computing. And the final area are some simulation optimization problems that are common in financial services, but also other industries as well.
Ben Popper: Yeah, that's really interesting to think about, you know, my level of, um, knowledge when it comes to programming is pretty low, but my ability to, uh, offer up metaphors is pretty high. So the, uh, you know, idea that when you're working in chemistry, if you were doing it in a binary digital way, you, you would have to have some abstraction, you know, that was kind of taking some of those ideas and, you know, playing around with them.
But if you were to do it in a, in a quantum way, yeah, there would be a better fit and almost organic fit. You know, our, uh, you know, uh, physical fit in the sense of physics between the two. And so the modeling or the simulation might be easier to, to run at a really sort of high pace and small micro, uh, I don't know how you, what you, what you would call it at that level of detail.
Blake Johnson: For chemists, for instance, they, a lot of the difficulty or chemicals, their assimilation depends on the number of orbitals of the, of the atom or the molecule that they're tried to simulate. And so, yeah, I mean, there are, encodings where you can directly use one qubit per orbital, and then there's a much more natural way to represent that sort of physical interactions with, with gates between, between qubits
Bob Suter: Also when you say chemistry, a lot of times we drop that term and people get these horrible flashbacks to high school. Right. With chemistry, but you do have to remember, I mean, you know, the things around you, there's, you know, foods, you eat, you know, food science, shampoos, all of these materials, these new alloys, these things like that. It's all based on chemistry, right? Um, creating new pharmaceuticals, um, eventually new antibiotics, it's all chemistry. It's going to take a long time to have quantum computers that are big enough or powerful enough. Yeah. But that's the direction. It's not just this academic interest in chemistry.
Ben Popper: And so when you talk about financial systems and doing modeling and simulation there, what are people thinking about?
What are they hopeful quantum computing could provide?
Blake Johnson: So there it's, we expect you'll be able to build different models that, that, that use optimization is kind of like the core engine there. It's, it's interesting in the sense that, um, You can prove sort of, uh, on pencil and paper that a quantum computer can offer at most a quadratic speedup.
And just in terms of speed of optimization tasks. There's a much more interesting game where I didn't say the jury is still out in terms of the quality. A lot of optimization problems are so hard for about the quantum and the class computers that, um, like exact solutions are basically impossible. And yet we have to solve these problems anyway, right?
Like the traveling salesman problem, maybe complete, but FedEx still needs to find some route to send their, their, their, their trucks out, deliver your packages. And so like, so people like, you know, make approximate methods to go out and do these. And there's a, a very fascinating race that's been going on in the algorithmic community about classical versus quantum algorithms and their ability to find better solutions, uh, with kind of similar runtimes.
And so. From the financial perspective, right? If you have a portfolio and you want to just find, uh, some allocation, for instance, you don't necessarily care if you've found the optimal allocation, which you, but if you found a better allocation that had a better return, you'd obviously take that. Right. And so I think there's a very interesting space for better solutions in the finance space.
Ben Popper: Yeah, I'm not sure we hit this at the beginning. I think we started and then we kind of dropped off, but sort of day to day. Can you talk me through, like, you know, what it is you work on? Are you working with teams who are writing code, trying to optimize algorithms? Are you working with some of that beautiful hardware?
Like what is your day to day?
Bob Suter: Blake does all that. Oh, I do. I do. I do different things. I've, I've been around around the industry for a few decades. I've I've been over 20 years in IBM Research before this role, I led the Mathematical Sciences Department, but I was also on the business side. So I look at this quantum program is something that's growing very rapidly.
In many ways. It's a brand new type of business, you know, we're, we're not tied into doing things exactly the way we used to do it. It's complimentary to classical computing, right? So I tried to do a combination of things of, of helping glue a few things together. So this, this rapid growth of what we're doing is painless, but, I think the teacher in me from a long time ago, as I love explaining and teaching quantum computing to people and, and to let's say non technologists, right? What the real value will be.
Ben Popper: How about you, Blake?
Blake Johnson: I have a great job, the sense that I get to let do a lot of different things. For some days, it's, it's working with our, our hardware teams, uh, developing the next generation of the control system, uh, that you know, is actually gonna actuate the gates, uh, and set some days we're doing software architecture with other teams and like the compiler stack that can compile, uh, a larger variety of programs, ones that have real-time classical competing as part of them.
Uh, and sometimes we're, we're just doing strategic thinking about which software applications or spaces are the most interesting most promising. And, and, and how can we, uh, adapt our, our development plans accordingly.
Ben Popper: And I guess, you know, one question that comes to mind by based on what you said is like, yeah, what is the size of the business now?
Like, to what degree is this still R and D you know, research, you know, with promising, uh, sort of opportunities ahead of it. And to what degree is this already a real business?
Bob Suter: So you mentioned quantum supremacy, which was a concept people and tossed around a couple of years ago. We prefer a more practical term.
Uh, which is quantum advantage. So here we have these quantum computers and by the way, they don't sit by themselves if they have to work with our classical systems. So the real question is when will these quantum systems do better than just the classical systems by themselves? At that point, you will have the traditional types of business models where people want to use these resources, want to use software.
That's built on top of that, and people will get paid. That is monetization in almost the usual ways will, will happen for the industry. We're several years away from that, from what I would say is quantum computers being used in full production in industry applications. Right. But that doesn't stop people with long-term research programs from engaging.
So we have the IBM Quantum Network, 150 different companies, startups, organizations. Um, I do want to do a shout out, particularly to startups in this area. We can help you get going, using real quantum computers. So we're doing a lot of developing the technology, but developing, you know, all aspects of the business ecosystem so that everything comes together just the right point.
Ben Popper: And, uh, I guess you had mentioned something in there that I'm curious about. You said, you know, you have quantum machines, but right now they still have to work alongside classical computers. What's the exchange there? Like what's the limiting factor that keeps the quantum machines from just sort of working on their own? Is it that they have to then feed back into a digital binary cloud that other people are using on their home computers?
Bob Suter: Blake, I want you to talk if you will, about the highly technical part, but I want you to do the simple part, which is, Ben look, if you're sitting at your laptop, right. And you are running a, let's call it a quantum application in Python. Well, Python is classical. Your laptop is classical. You send the job up to the cloud.
Well guess what's going to agree that at that end? Classical computers, you were getting, going to get into a queue to run on a quantum computer, classical computers, right. All the way. And that will travel all the way until you get right before the classical correction, right before the quantum computer, the control systems.
And so here, I'll hand off to Blake to, to say what kicks in then.
Blake Johnson: I would say like in general, right? The quantum computer is like, as Bob mentioned, since there's a different paradigm of computing, right? There's certain applications, certain workloads that are just never going to be a good fit for quantum.
I mean that the magic of quantum speedup is when you can use the phenomenon interference effectively to do, you know, you make wrong answers, cancel out while the right answer is to amplify. And if you can't like create things that have that flavor in your quantum algorithm, then you, you're not going to have a speedup.
And so like, if you just want to add two numbers and you don't need to get, have fancy superpositions and interferences going on, uh, you might as well just do that with a classical computer. And so like there's there's tasks, which are well-suited to quantum machines and the ones that are better suited to classical machines.
And that's particularly a big case because the quantum machine is almost always going to have constraints in terms of clock speed. They're going to run at a slower clock rate. Are they going to have fewer qubits rather than the classical. Classical bits are so cheap. They're so, so cheap. And so like, if you have a problem that doesn't need the economy resource, then you, then, then you're not going to put it there.
Can you give
Ben Popper: me an example or a little bit more detail about this idea of a problem where. You want wrong answers to cancel out and good answers to sort of join together? What does interference mean? You know, obviously I'm a lay person. What's a good example of a problem that would require that and, and how that sort of functions?
Blake Johnson: So, I mean, this, this goes to the heart of like one of the big myths about quantum computers that they execute everything in parallel, right? It's not just classical computation done in parallel. It's because when you measure the bits at the end of your quantum computation, you measured the qubits you get a single answer out.
And so if you just run classical computations in parallel on your, on your classical and your quantum hardware, it'd be equivalent to just writing one of those classical computations. So like in order to exploit the phenomenon of quantum mechanics that you get with your quantum machine, You need to engineer interactions between those different paths.
Right? And so it's hard to say more than that. Other, you know, waving my hands at this without, you know, getting into the math of a specific example, but, you know, we've, we've now developed a, sort of a tool bag of how you can construct algorithms with this flavor.
Ben Popper: Okay, well maybe, maybe for the show notes or something, you could point us to an example where there's like a longer description or something, because I'm sure people would be curious to know.
Blake Johnson: Sure. But I guess I would say like one of the things which, you know, recognizing that this, you know, quantum classical interaction, isn't a temporary phase of kind of competing, but it's actually like a permanent fixture of like, all that quantum computing forever will, will involve interactions between quantum classical.
You know, it's, it's important to figure out how you, how you make that marriage happen in an effective way. And so one of the things that we're really excited about over the next couple of years is what we're doing with this Qiskit runtime, which is a, a way to sort of bring the classical computation closer to the quantum hardware, to allow users to express programs, which will have these interactions between quantum and classical, uh, resources, uh, which is kind of at the heart of a lot of applications.
Ben Popper: So one tradition we have on the, uh, Stack Overflow Podcast is at the end of an episode is a wrapping up. I read a lifeboat badge winner, which is somebody who took a question on Stack Overflow that had a negative score, gave an answer. And now the, the question is up to a score of 20 or more. So they kind of saved a, a piece of knowledge from the dustbin of history.
But today I'll jump over to our Quantum Computing Stack Exchange. If any of this means anything to you, please people need help. It means nothing to me. How has the Grover iteration realized in an actual quantum some circuit, how to calculate the average fidelity of an amplitude dampening tool? Is there a different way to represent polygates in X basis?
There's some questions in there that need some help, so I'm not going to demand it, but. If you got a little free time today, people people could use your help. Okay. I'm just kidding. Alright. Well, yeah, this was super interesting. I want to say thank you so much for reaching out and offering to spend the time with me, for me, it's exciting to learn about this stuff and I'm sure for our listeners, it's fascinating to hear about some of the big tectonic changes and future possibilities that would come with this technology.
So I'm Ben Popper, uh, Director of Content here at Stack Overflow. You can always find me on Twitter @BenPopper, and you can always email us podcast@stackoverflow.com.
Blake Johnson: And I'm Blake Johnson, I'm the Quantum Platform Lead at IBM Quantum. You can find me on Twitter at Blake Johnson. If you're interested in learning more about, or just getting started with quantum computing, I recommend you check out our Qiskit textbook at qiskit.org.
Bob Suter: And I'm Bob Suter, @snarkyandroid on Twitter, but you can find me on LinkedIn as well. I happened to have written a book about quantum computing. It's called Dancing With Qubits and for all those of you who, uh, may have forgotten the math you needed, but one actually get into the subject and know what you're doing. Uh, I think it's a fairly good introduction.