Alex Khan, Quantum Computing Entrepreneur and CEO of ZebraKet is interviewed by Yuval Boger. Alex and Yuval talk about Alex’s book “Quantum Computing Experimentation with Amazon Braket,” his experience working with different hardware and software packages on Braket, the best way for optimization experts to get into quantum computing, and much more.
Yuval Boger: Hello, Alex and thanks for joining me today.
Alex Khan: Hi Yuval. It’s a pleasure being with you.
Yuval: So who are you and what do you do?
Alex: Well, I’m Alex Khan. And at the moment I’m an entrepreneur in quantum computing, though my journey started with engineering and physics. I was a dual major in college. Eventually, I got into IT, built automation solutions for claims processing, document automation solutions for policy generation. And then I got my MBA, which is kind of story in itself, and then became AVP or business technology for Lincoln Financial. So I saw both the solutions side, providing solutions, and also the corporations and how they evaluate solutions and their strategy to bring in new technologies. So in 2018, I found out about quantum computing through the D-Wave leap program. And to me, that was just another technology that I wanted to learn about and get into, and so that’s where my journey with quantum computing got started. And really, it’s from the perspective of, is it a technology that we can use? Is it useful for companies? And that’s my perspective.
Yuval: And you have a recent book out. Could you tell me a little bit about the book and what made you start writing it?
Alex: Yes. The book was a year or more of considerable effort. Initially, I had said I’m not going to write a book because it’s usually a lot of work and I was already very busy, but Packt Publishing called me up and encouraged me to write the book and I decided to write it because, over the last few years, I had learned some lessons on how to get into quantum computing, how to make use of quantum computing, through various experiments and projects that I’d worked on. And I thought that would be a different perspective that I don’t see a lot in the quantum computing industry. We talk about the physics or the circuits, but we don’t [talk] a lot about a process of actually making use of quantum computers as they are right now. There’s a lot of theory, but I’m more hands-on. I have a hard time hyping anything. So I have to get hands-on and see what something can do before I can promote it, and I thought that would be a good lesson and information to put in this book. And the reason it became more about quantum computing experimentation with Amazon Braket is when I was starting, Amazon Braket actually was the service or the platform that made various quantum hardware available. I started out with Rigetti, IBM, I couldn’t access IonQ at that time.
And even Rigetti was a little more complicated to access directly. So when Amazon Braket came out with pay per use service, I was very excited about that because I could just use the Ion trap, I could use Rigetti and I didn’t have to worry about a lot of the SDK and setup and all of that. So I thought that was a good platform to get exposed to multiple quantum computers. I thought it was somewhat efficient and I thought it would be able to allow me to bring my optimization, which is all of the experimentation I had done; optimization using D-Wave into both Annealing and then gate quantum computing and kind of make it a nice cohesive story in that area. So that’s what ended up happening.
Yuval: So to the million dollar question: if I’m an enterprise customer today, could I do something truly useful on Amazon Braket, whether it’s optimization or something else or whether the state of the service right now is more about learning and experimentation and getting to know quantum a little bit more?
Alex: Yeah, it’s certainly about getting to know quantum. I mean, it’s taken me three years to get comfortable with the technology and how it can be potentially used, and just explaining quadratic relationships to others is a challenge. Having people understand the difference between a linear solver and a quadratic solver and the benefits of quadratic relationships is… I mean, people don’t think in those terms and most people in the industry are using a tool, there’s very little optimization. I mean, my whole career, we never optimized anything. So optimization is something new, combinatorial optimization is new, quantum computing is new, and the potential benefits of this technology is not that easy for people to grab. So I would say the first step is just to get comfortable with that. Now how far can quantum computers go with it, and can we actually get something useful?
I would say, yes. I mean, I’ve had enough experiments. There are models, I wouldn’t say that they are industry-level problems, but there are models of real-world problems that can actually be solved. I mean, they can be solved on D-Wave, [and on Gate model] with QAOA. They can be solved on simulators and in the book, I showed that you can get reasonable results even through Rigetti or IonQ, as long as you stay within the framework of what those systems can actually solve right now. So my goal isn’t to show that, oh, okay, quantum computers can only solve certain set of problems, my goal is to see just what can we do right now. Stay within the tool that you have. I mean, I used to have microscope, which I could see only so far. It has zoom of a certain number, and I used it for whatever it could do. And I’m doing the same thing with quantum computers, just using them right now for what they can do.
Yuval: So if I’m a classical optimization expert, I’ve been doing optimization all my life, hypothetically, and I hear about this new quantum technology, how deep do I have to get into quantum to do something useful with it? Do I have to understand matrices, tensor math, and gate level? How deep do I have to go to do something useful in optimization using quantum technology?
Alex: Yeah, that’s a good question. So industrial engineers, industrial scientists that are doing optimization are using linear solvers. They have a very good understanding of how to optimize within certain constraints. And it’s a well-known and very broad discipline. I didn’t go into industrial engineering. I had no idea what that was, but having gotten into optimization now, I mean, I have, I guess, a stronger appreciation of it. So it is a different discipline. And those people from that background do have a good understanding. I would challenge that I think a lot of the equations are more linear programming type of equations. The quadratic part has been, to some extent kept out, or even higher order. I mean, you go from second order to third order, like power of three, those kind of equations have been kept out of these solvers because traditionally the solvers couldn’t actually solve these problems efficiently.
So that’s the whole point of the linear programming. So I think even that group of people can begin to see the world now with quantum computers coming out and look at those quadratic relationships. And for example, with Microsoft QIO, you can go to higher order problems. So the question is, can we recognize these higher-order relationships in our real world? For example, we talk about bundling. If you buy one item, you want to buy some other item with it. Is that important? Is that not important? Because you can bring out those quadratic or pairwise relationships from the real world and you have to think about is that useful for you? Because if it is useful, then these quadratic solvers or QUBO solvers will be able to actually give good results, eventually have an advantage over the linear solvers.
Yuval: If I understand you correctly, the optimization expert, the classic optimization expert, would want to understand what QUBO is or other non-linear optimizations would have to understand how to express real-world problems as a QUBO or other optimization problem. But once you do that, you don’t necessarily have to understand whether it’s in an annealer or a trapped Ion or a superconducting computer. You don’t have to understand what a Hadamard gate is. Is that a fair statement?
Alex: I mean, at this point, no, I think eventually that would be a fair statement, especially, I mean, there will come a time when you don’t have to worry about the circuits for gate quantum computers, but for now, it depends on the technology you’re using. So D-Wave has come out with a number of different toolkits for the hybrid solvers, where you can use their binary quadratic model, their CQM, which is a new model, which allows you to just put down the actual objective, the variables, and the constraints and behind the scenes, it develops the QUBO. So you don’t have to worry about developing the QUBO. Now I would say that’s still good for prototyping, if you were actually building a solution for the industry, you would want to know how to build the QUBO and optimize your own QUBO rather than give it to a generic algorithm to build the QUBO itself. But it’s great. I mean, you can very quickly get answers and you don’t have to go very deep into building the QUBO.
With gate quantum computers, there isn’t a tool available that will take your real-world objective function and create a QUBO. You would have to build a QUBO. You would have to understand that the QUBO is going to go into QAOA as a… I mean, you’re going to build a QUBO or Hamiltonian, you’re going to run it through QAOA. So you have to know a little bit about the mechanics of QAOA. QAOA isn’t a single solver where you just give it a problem, and it’ll solve it and give you the perfect results. I mean, I think you have to, at this point, kind of know QAOA, know its limitations, work with it to optimize it, get the right parameters. So yes, you have to know ’ little bit about opening up the hood and looking inside the engine and tweaking it a little bit to make it work properly for you.
In the future, as the gate quantum computers become more powerful, the algorithms are more automatically optimized, or maybe there’s better ones than QAOA, then one might not have to worry about that. And maybe at that point, people have built some layers where for the gate quantum computers, you can have 50,000 variables, like we can on a D-Wave’s hybrid model, and it’s competing with linear programming solvers where you don’t have to know all the insights, but right now I think you do have to. And that’s one of the reasons for me… I mean, I spend multiple chapters explaining QAOA and exactly how QAOA actually is optimized and what kind of results come out of it. As you change the parameters, I show how the probabilities of the best solution or the minimum value begin to increase. And I actually visualize all of that. So it’s not just about the equation, just it’s like getting an intuition on what QAOA is doing.
Yuval: When I look at some of the book chapters, for instance, chapter [9], in your book, you write about various hardware platforms. Putting D-Wave aside looks like you did some work with Rigetti, maybe IonQ others. Did you see any meaningful differences between various gate based computers, or should the user not have to care about that?
Alex: They definitely have to care about it because they’re so different. The Ion trap, the ones that I had access to is 11 qubits. So now you can have access to the 20-qubit Ion trap. And it’s fully connected graph for the Ion trap versus with Rigetti, I had access to the M1 which was 80 qubits. Now they have M2, which is also 80 qubits, but it’s very loosely connected or there’s a very few connections between the qubits, so you can only put in a very sparse matrix on that model. So that would be like solving an equation where you have very few quadratic terms. And in the book, I basically take the actual graph of the Rigetti system and then use that graph as a starting point for the equation.
So I’m basically using Rigetti the way Rigetti is designed. This is my analogy with the microscope. I mean, if I’ve got a microscope, I’m using it exactly the way it works, I’m not trying to make it do anything it doesn’t actually do. So I’m using those different devices for what they’re meant for. In one case, you have 11 qubits fully connected, so I give it that problem. In the other case, it’s 80 qubits, very few connections between the qubits so I give it that problem. And when you look at it that way, you begin to actually see the value of what has been engineered so far. Now, of course, as the connections increase or there are quality increases, things will get better. And with the current systems, I mean, I show that you can see the best answer using QAOA and those answers show up. But if I give Rigetti, for example, a problem with a large number of quadratic relationships, or try to do multiple iterations or Trotterization of QAOA, there’ll be so much error we won’t see any results, but that’s not the point. I mean, just seeing nothing doesn’t help. It is good to see something and know which direction to go.
Yuval: So if the users need to understand the hardware, to what extent do they need to be proficient in multiple software packages? I see in the book that you cover PennyLane and Qiskit, and maybe a couple of others, does it matter to the users in your opinion?
Alex: Well, I tried to only focus on the SDK or tools that Amazon Braket has brought in. I mean, the book was about Amazon Braket. I took the approach of writing my own code for everything. I didn’t use any libraries. And even for example, in Amazon Braket, you cannot build the Bloch sphere. So I could have used Qiskit Library to build the Bloch sphere and then somehow translated the circuit for Braket, but I really wanted to stay purely within Amazon Braket’s framework. So I actually had to write the whole code for building the Bloch sphere myself and that code is with the book. So the point being that Amazon Braket team is adding a lot of different libraries as they feel are useful for their customers. PennyLane is one of them. They’ve now added a plugin for Qiskit. So they’re going to expand the Amazon Braket service and bring in different libraries and you can bring in PyTket or all kinds of other tools.
And also for someone who’s actually in quantum computing, I mean, there’s no limitation. I mean, you can use all kinds of different tools. You could use Classiq, build a circuit, then decide to send it to a Braket device, or you could be working in DevOps on with the EC2 services, using some other service in Amazon web services, and then pull in data from there and bring it to Braket. So the Braket team is not saying that you should work in Braket in isolation, but in order to teach about Braket, I wanted to make sure that I focused in on what is purely Braket. So with that, yes, I mean, there are so many packages for natural language processing, quantum machine learning. I mean, there’s so many other packages, that’s up to the user to decide how do they want to integrate a different package with the features and functions available in Braket and make use of the devices in a pay-per-use basis if that’s of value to them.
So the book is really focused on Braket and what you know can do out of the box, but there’s a lot you can do depending on your knowledge. And I mean, a lot of people started out with Xanadu, through the QHack hackathons, some people started out with IBM, others started out with machine learning so they can all eventually decide if Braket is an extra service they want to add to what they’re already doing. But the main purpose was for people who are already in the industry who are using Amazon web services, Braket just becomes another service for them to add. I’m not… So I think it’s up in the air about how will people who start out with Qiskit or some other tools find their usefulness in Braket? But I mean, those are connected now.
Yuval: You mentioned that it took you a year to write the book. So you’ve written a lot of text and a lot of code and a lot of hours experimenting with various packages. What’s the thing or things that surprised you the most during that process?
Alex: Regarding my own skills or regarding Braket? There was just a lot to learn. Initially, I had a vision for what I wanted to do with the book. I wanted to get deeper into other use cases that I had done, like portfolio optimization. I’ve done COVID optimization. I’ve done a number of different experiments with Shor’s Algorithm using both Qiskit and the Amazon simulators. So I wanted to get that far. I wanted to really go to that level. And as I started writing the book, what I realized is just the steps it takes to educate someone or the way I wanted to educate someone, I didn’t want just to breeze over stuff, give equations and move to the next chapter. I really wanted to get people to get hands-on and have a feel and intuition for how each step works and get comfortable, for example, with Quantum Fourier Transform or working with phases. Then with QAOA, I wanted them to actually see how QAOA, by changing parameters, begins to show better results for the minimum, and how it brings out the minimum.
So, as I went through that process, I actually ran out of pages and ran out of chapters on how much I could actually put into the book. And it would’ve been another six months to what I wanted to do originally. So I do [did] cut a lot of material, and then the book ended up really becoming about Braket and getting to the point where you are ready now to actually have a real conversation on use cases. So I think my learning was number one, writing a book is very hard. We had to go through so many edits. My analogy of writing a book is pushing or moving sand upstairs. So every grain of sand has to make it up, but it was a very useful exercise also in just gathering my own thoughts on things that I thought I knew or things I had read and things that when I didn’t have a library to actually use, and I was determined to write it myself, then I really had to get down to the theory and the details on how that thing worked. So it was a great learning experience, obviously for me, as well.
Yuval: As we get closer to the end of our discussion today, you mentioned that you were AVP, I think, at Lincoln Financial. A company like Lincoln Financial, when should they get into quantum? Should they do it a year from now? Should they do it a year ago? Should they start now? What’s your perspective?
Alex: Well, my general sense is that companies like Lincoln Financial are already at least hearing about quantum computing. I’ve talked to the CEO of CareFirst, which is a Blue Cross, Blue Shield or health insurance company. And he already knew about quantum computing. And if you talk about the VP of architecture, these are people who are supposed to know what is out there. So I would expect a company, like Lincoln Financial, or any company to at least be looking at listening and being aware of the technologies and where they are. Very quickly, these people are going to find out that, most likely, this technology is not ready for them. And that’s kind of an evaluation I used to do, my teams used to do, we were always looking at what do our customers want? What are our goals? And does the technology fit that?
So if the technology is not ready, it’s not ready. Now, that’s not to say that some companies won’t have an entrepreneurial or a group that supports startups and start having conversations with them and maybe support them. That’s more giving back to the community. That’s not really of use necessarily for the company. I mean, when I was AVP of business technology, I was responsible for the portfolio of the company. So this is project portfolio, not the financial portfolio, but it is a financial portfolio as well because you’re making choices on if you bring in a new technology, that’s cost associated with it, and what are the benefits? And every officer or every business sponsor has to be able to prove that the technology that they’re bringing in is going to have a value or a return. So I don’t think quantum is there where somebody could bring that into… If I was the portfolio manager for that company and prove to me that somehow that technology is going to have a benefit for the company. So, I mean, I think right now it’s still in the early stages.
We have to prove out… I mean, I’ve seen under certain use cases, you can get some good answers, but is that use case of value to a company? And where is that of value? So if you see some of the examples like in D-Wave, D-Wave has hundreds of use cases that they have put on their website and on YouTube. Some of those use cases, like the one by SavantX on the port of Los Angeles, they were able to find a specific use case where they could get faster results and give value to the truck drivers, which was not available before, but it took them a while. I think it took them three years to find that specific use case and show the clear advantage. Now, that is not to say that somebody else can’t come up with a supercomputer or a genetic algorithm and say, hey, I can get the same result. But the point is they got there with an optimization solution using D-Wave that serves the purpose. And that’s, I think, what we have to look for, we have to look for in these financial institutions and supply chain a solution that is valuable enough and maybe was delivered by a quantum technology, even though maybe somebody else can do it also, but at least you get there first with a solution.
Yuval: It sounds like a time for experimentation, which dovetails with the title of your book, Quantum Computing Experimentation with Amazon Braket. Alex, how can people get in touch with you to learn more about your work?
Alex: I’m actually in quite a few places. So I think the best is to just do a Google search for me, Alex Khan quantum. I think that usually puts all the links out there, but the easiest place is usually through LinkedIn. I’m available on LinkedIn a lot. I do chat with people on LinkedIn. So if anyone wants to connect with me, that’s the best place.
Yuval: Excellent. Well, thank you so much for joining me today.
Alex: Thank you, Yuval, it’s a pleasure.
Yuval Boger is a quantum computing executive. Known as the “Superposition Guy” as well as the original “Qubit Guy,” he most recently served as Chief Marketing Officer for Classiq. He can be reached on LinkedIn or at this email.
September 28, 2022
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