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<\/a><\/p>\nBill: So my name is Bill Wisotsky. I work for SAS. I\u2019ve been there for about 24 years. Most of that time has been in professional services. I started quantum there about four years ago, and it\u2019s progressed to where it is today, where we actually have a dedicated team and I\u2019ve been moved over to R&D as a principal quantum systems architect.<\/p>\n
Yuval: And for those that don\u2019t know, SAS, that\u2019s not the airline, right? That\u2019s not the British Special Forces. Tell me just a little bit about SAS.<\/p>\n
Bill: So SAS has been around since the early 1970s. We\u2019re the leader in the, anything analytical, right? So machine learning, AI, we do business intelligence, data management. Our solutions are used by virtually almost every company that is doing some sort of analytics. And you missed the shoe company. We\u2019re not the shoe either.<\/p>\n
Yuval: And you\u2019re not software as a service either, right? So it\u2019s not S-A-A-S and you\u2019re not S-A-S-S, right? So it\u2019s really just S-A-S.<\/p>\n
Bill: S-A-S. Either all-capital or all lowercase.<\/p>\n
Yuval: Very good. What was the insight that led SAS to go and start exploring quantum, I think you said, four years ago?<\/p>\n
Bill: Well, I mean, from my perspective, I\u2019ve always been interested in quantum. When I was in graduate school for behavioral neuroscience, my research was in biophysics doing EEG recordings, specifically something called visually evoked potential on the human cortex. And I had to learn about the way photons were being absorbed by the retina. So I had to learn a little bit about quantum physics, and I was introduced to the double-slit experiment. And it turned into almost like a rabbit hole where I was so interested in what I was learning about quantum physics and I was spending more time doing that. Fast forward, around COVID time, obviously everything shut down. My kid\u2019s sports were all canceled. I had a lot of unaccountable time. And I said, \u201cLet me explore this concept of quantum computing.\u201d And I realized it was more advanced than it was years ago when I first researched it. So I started playing around with it. I started teaching myself some of the software development kits and figuring out how I could integrate it with SAS. And, um, I saw that, you know, some of the real promising areas of quantum computing around areas such as optimization and quantum machine learning, you know, that\u2019s in our wheelhouse, that\u2019s our bread and butter. So it was a natural sort of marriage between the two.<\/p>\n
Yuval: So in your case, in your company\u2019s case, it was sort of bottom up, right? You knew about the potential or was it some directive from the CEO, \u201cHey, we should really look at this quantum thing.\u201d<\/p>\n
Bill: Yeah. I kind of went off on my own when I started doing this and I kind of navigated through SAS to try to figure out who to talk to, to sell this idea to. And it was presentation after presentation and proof of concept after proof of concept. I wound up in a department called the Solutions Factory that bought into this. And they were the executive sponsors, and we did a whole bunch of more organized proof of concepts, I should say. And eventually, the chief technology officer saw it, and he came on board, and here we are.<\/p>\n
Yuval: So I very much want to hear about how it\u2019s going, but let\u2019s actually talk a little bit more about how it started. So you said you studied biophysics, but I don\u2019t think you have a PhD in physics, right? And so you didn\u2019t have a PhD in physics and you had to convince the company that this is worth something, this is worth looking at. What lessons have you learned or what advice can you give to a friend who is in a similar situation, sort of similar educationally and similar to the company, how to get going and how to get started in a business?<\/p>\n
Bill: So you\u2019re right about my PhD. My graduate program was in behavioral neuroscience. And I originally went into that to do animal behavior and evolutionary biology. And after working in an animal lab, I decided that\u2019s not what I wanted to do. So I switched over to humans, and biophysics was the lab that interested me. I actually never completed the PhD, because we got involved and we started a biotech company that ultimately kind of went south. And then I wound up working for SAS doing predictive analytics. I always was interested in quantum ever since my dissertation. I mean, I was all the way up to the point of just defending it for the most part. I just had to write it, do some edits. And it was a hobby of mine for years. I kept on reading about it. I would read textbooks. I\u2019d read papers, all from, you know, I\u2019d say early 2000s all the way up until, you know, when I started this. So, if I was to give advice, I would say to anybody, be very persistent, learn the topics, speak from some level of education. You don\u2019t have to be a physicist to understand quantum computing, and keep pushing forward. It\u2019s sometimes a longer road than others.<\/p>\n
Yuval: You\u2019ve been doing this for four years. And if I remember correctly, we met after I heard you speak at a D-Wave conference about a case study. Um, tell me about some of the exciting projects that you did in these four years, some of the successful, or maybe the not so successful ones.<\/p>\n
Bill: Yeah. So some of them I could talk about in detail, others I can\u2019t. At D-Wave, we did this sort of project called the Kidney Exchange Project, where we were looking at donor and recipient pairs and how one donor that wants to donate a kidney to a recipient might not be compatible. And it creates an \u2014 so the donor one could, you know, actually donate to somebody else that they are compatible with, and it\u2019s called a swap in a way. And that network could be very, very large, and it has a lot of constraints, and it has some objectives. So it turns into a very large combinatorial optimization problem. And we quickly realized that when we ran this through quantum, we got a distribution of results. We got good results, but there was no, you know, guarantee that the result we were getting was the absolute best. We can only figure that out from a classical perspective. So we would take the results from the quantum solver, in this case it was D-Wave, and we would use them as warm starts into our classical solver. And then we were able to solve the problem in a fraction amount of time and prove optimality. From there, we went on to do the same technique on some other proof of concepts around optimization in consumer packaged goods. We did, we\u2019re working on some POCs in the quantum machine learning arena around fraud detection and around bankruptcy modeling. And we\u2019re also doing disaster response with you. We\u2019re working on some projects along with yourself.<\/p>\n
Yuval: Some of these POCs, I\u2019m guessing, are non-internal POCs, but you work with customers, clients, Do you tell them this is quantum? Do they care or do they get excited or turned off when you say, \u201cHey, I\u2019m actually going to use this quantum computer thing?\u201d<\/p>\n
Bill: Yeah. So that\u2019s a good question. The only non-customer POC was the kidney exchange problem. And that\u2019s because it\u2019s a well-known problem. Every other one was some sort of customer-driven project. Now, when we tell customers, we tell them that it\u2019s a quantum project. That\u2019s how we wound up doing the POC, and they\u2019re very interested, and they\u2019re very excited. We go into this more in a sort of collaborative way where we don\u2019t quite know what\u2019s going to come out of the project. They don\u2019t quite know what\u2019s going to come out of the project. And we work with them from both the quantum perspective and the classical perspective to see what might come out of it. And it\u2019s a learning experience for both sides. But the amount of understanding and appreciation from customers. They\u2019re very excited about quantum and their expectation level is very, I wouldn\u2019t say low, but it\u2019s very negotiable, right? They\u2019re not expecting to solve every sort of complex problem. They know that quantum is in the beginning stages and could only be used for very specific things. So in a way they\u2019re fun.<\/p>\n
Yuval: Have you seen quantum advantage? I mean, either a problem that you couldn\u2019t solve classically or was too long to solve classically or an area where something like this warm start that you were describing actually delivered tangible benefits?<\/p>\n
Bill: Yeah. So, you know, that kidney exchange problem, for example, to solve that to optimality \u2014 it took hours to solve. In the hybrid approach, we solved it in 30 seconds. So that is one area if you want to define quantum advantage of speed. The funny thing about working with customers at SAS is we get a little bit more freedom in how we define quantum advantage, right? So you hear these articles about some quantum company solving problems in two hours that would take 45 million years or whatever the number is. But it\u2019s not only about speed in our definition. In our definition, quantum advantage is something that the customer will benefit from. It could be speed. It could be the expressivity of a quantum machine learning model by being able to project classical data into higher dimensional representations and then get a more predictive model, even though that model takes longer to run. That might be a level of quantum advantage. So with that being said, in some of our data sets that we\u2019ve used around quantum reservoir computing and quantum support vector machines, we\u2019ve seen some levels of quantum advantage, not necessarily about speed, but when you\u2019re measuring things like area under the curve, we\u2019ve seen some benefit. We\u2019ve also seen benefit in being able to train these models with less data. We start off with small data sets, and then we move, you know, we get larger and, you know, quantum seems to stay very similar and classical varies with the size of the data sets.<\/p>\n
Yuval: One of the things that struck me when we were working together, I mean, SAS was doing a project with QuEra, my company, is that you were asking about, or your team was thinking about optimality. Sort of quantum gives a solution, but it may not be the optimal solution. Um, do you find that customers are looking necessarily for the optimal solution or they just want something good enough within a reasonable amount of time?<\/p>\n
Bill: It depends on the customer and the field that they\u2019re in. In some fields, they need a 100% optimal solution and they don\u2019t care how long it takes to get it. Um, usually there\u2019s more at stake if the solution is not optimal. Um, in other cases, it\u2019s about speed. They just want a really good solution, but they need it very fast.<\/p>\n
Yuval: But with quantum, I don\u2019t think you\u2019re able to prove optimality.<\/p>\n
Bill: No, and that\u2019s where that hybrid process comes in, right? So when we take those solutions, so what we could do is if when we get our sort of distribution of solutions, and we have no idea how close to optimality they are, we could feed them into our Viya software in parallel and have Viya solve them, basically all at the same time with that warm start. So each one of those Viya processes will have a different warm start from each one of those solutions that we\u2019re solving in quantum. And then we\u2019re able to run that to optimality and prove optimality at that point.<\/p>\n
Yuval: When you solve a problem using classical means, I\u2019m sure by now you\u2019ve developed, you and as far as the company, sort of a workflow. Okay, here\u2019s how we capture the problem. Here\u2019s how we think about expressing it. Is that any different in quantum? How does that work for, look for you when you try to solve a problem using quantum computer?<\/p>\n
Bill: Yeah, you know, it\u2019s interesting you say that because it\u2019s a much harder thing to do that in quantum than in classical, right? In classical, it\u2019s tried and true. We have all these different, you know, algorithms. We have X number of years of experience. We have all these specialists in the field. In quantum, it\u2019s almost like sort of the event horizon of a black hole. You kind of not know what\u2019s on the other side. And in some cases, we found that quantum works really well on certain problems, and then we take similar problems and quantum doesn\u2019t work as well on those similar problems. What we\u2019re finding is problems that have like a high combinatorial relationships amongst the variables. When that solution space is very large to search, we\u2019re able to get a solution relatively quickly. And that\u2019s from an optimization perspective. In a machine learning perspective, it\u2019s a little bit more of an unknown and we have to kind of test different things. But it\u2019s, and that\u2019s why I said before, I\u2019m gonna work on customer POCs. It\u2019s very much a collaborative project where we have to understand the problem classically first. We need to understand how this works classically, where the areas in that classical process are very computationally expensive, take a long period of time, what the problem formulations look like, and then we have to kind of pull that back and say, could quantum help in these specific areas? And that\u2019s how we kind of, you know, are looking at these things.<\/p>\n
Yuval: Would it be correct to say that today quantum is just another arrow in your quiver, just another tool in the tool set, so it gives you another option on how to tackle a particular problem?<\/p>\n
Bill: Yeah, I mean, we see it as another, you know, what we call a PU, right? We have CPUs, we have GPUs, and now we have QPUs, right? I mean, we don\u2019t see it as really any different, and we want to take away some of that complexity from customers, right? A customer today, if they\u2019re running a deep learning model in SAS, they\u2019re not looking at the deep learning model and necessarily caring if it\u2019s running on GPUs or CPUs. They just want the model to finish and as fast as it could with as much predictability that it can. We see quantum fitting into that paradigm as another sort of way of processing a problem.<\/p>\n
Yuval: Do you have a favorite quantum computer? And the Lego quantum computer that you have is not a legitimate answer here. Um, and you don\u2019t have to say QuEra, of course, a favorite. And why is that computer your favorite? What makes it, uh, sort of your first choice? If, if there is one.<\/p>\n
Bill: I don\u2019t, I don\u2019t have a favorite. I\u2019ll be honest. And I find that there are strengths and weaknesses across the board. What we\u2019re trying to do is we\u2019re trying to look at different modalities of quantum computing, right? We\u2019re looking at neutral atoms. We\u2019re looking at trapped ions. We\u2019re looking at photonics and superconducting because they all have their sort of strengths or weaknesses. They all fit into the quantum puzzle in different ways. And then we look at vendors within these different modalities and then we choose which ones we want to partner with. Yeah. And, and yeah, there\u2019s no favorite really.<\/p>\n
Yuval: But what makes a vendor a good one for you? Is it the team that you work with? Is that the quality of the documentation, the availability of the machine? I mean, how can a vendor get on your good side?<\/p>\n
Bill: It\u2019s a little bit of everything, right? It\u2019s a little bit of, um, you know, we take a look at the quantum computer, the hardware itself, and we see which one is, you know, the most reliable or most advanced. We take a look at the software development kit that we have to integrate with and we have to use and the documentation. But really what\u2019s very important to us is the team that we wind up working with from the quantum manufacturer. If the team is kind of not available or standoffish in some respects, it\u2019s harder for us to do our development and work with them. If the partner is very engaging and easy to work with, that\u2019s a big plus. It\u2019s very important.<\/p>\n
Yuval: You\u2019ve been doing this for four years. What have you learned about quantum in the past year that you didn\u2019t know before? What\u2019s new in your mind?<\/p>\n
Bill: I\u2019m learning things every day. I\u2019m learning new things about neutral atoms, things that I didn\u2019t know before. I\u2019m learning things about trapped ions that I didn\u2019t know before. But the biggest thing that I\u2019m learning is that you can\u2019t stop learning. When I first started with this, they were talking about quantum computing being 20 or 30 years away, but that sort of horizon keeps on getting closer and closer and closer. What I\u2019ve learned in the last year is that every quantum manufacturer is pushing that horizon of sort of error-corrected qubits and large-scale quantum computers ever closer to the present. So, you know, I think that is one of the biggest take-homes. Another thing that I\u2019m learning is that there are ways of achieving some level of quantum advantage without having these large quantum computers with tremendous error correction, that there are ways of getting various levels of what we, I guess, would call quantum advantage. Just have to be a little imaginative, I guess.<\/p>\n
Yuval: When you think of SAS competitors, do you also see them using quantum or do you find that SAS is ahead in that respect?<\/p>\n
Bill: I mean, I don\u2019t know if they\u2019re using quantum or not. I mean, there are some vendors that are more niche vendors than us that don\u2019t encompass such a large breadth of solutions. And they\u2019re involved in quantum a little bit, but I don\u2019t know if I would call them competitors per se.<\/p>\n
Yuval: I think you knew this question was coming, but I do have to ask you a hypothetical as we get towards the end of our conversation today. So if you could have dinner with one of the quantum greats, dead or alive, who would that person be?<\/p>\n
Bill: I think Roger Penrose. I\u2019ve listened to him talk and I think that some of his ideas are pretty intriguing. One of his ideas, this Orchestrated Objective Reduction theory, it hits home to me because it\u2019s all about consciousness and microtubules and neurons and quantum superpositions and entanglements that happen on the nervous system. And because my background was in behavioral neuroscience, I remember learning about action potentials and graded potentials and how On\/Off receptors and parvo or magnocellular sort of arrays in your brain process vision. And it always got to a point where there is a hard cutoff between sensation and perception. And there was a lot of theories about how that kind of, you know, you could build up all these on-off receptors to make lines and shapes, but that sort of switch over to perception was always sort of theoretical. Well, it brings, you know, it connects various higher order cortical areas of the brain. And I never liked that. And then when I read about this Orchestrated Objective Reduction theory, it was really the first one that I ever heard about that talked about a quantum theory in neurons that was responsible for consciousness to some extent. So I thought that was very, very interesting. So I would love to be able to pick his brain about some of this stuff.<\/p>\n
Yuval: You know, but as I was listening to you, I think there\u2019s a question I forgot to ask. You mentioned that you moved from services to sort of an R&D position related to quantum. What\u2019s the difference in, what\u2019s the difference as it relates to SAS and what are you going to be able to do in R&D that you weren\u2019t doing in services?<\/p>\n
Bill: Yeah. So services, um, I wasn\u2019t doing quantum in services. I was doing quantum on a part-time basis. I was doing it at nighttime after work or before work or at free time, but I had, I had customer engagements that I was working with that had to do with predictive modeling or data access or business intelligence or just performance issues that might arise when doing things on certain types of servers configured in certain ways. So this is allowing me to do quantum full-time. And my position now is it\u2019s kind of varied in a way. I work with the developers on these quantum algorithms and what they should do and how they could get incorporated into SAS. I also work with product management as to what the\u2026 when we come up with our first stage of beta products, what they should look like. I work with partners like yourself. I work with academic institutions on joint research projects, on presentations, on guest lectures. It\u2019s a fun role in that sense. I get to really get my hands really deep in the algorithm development, as well as, you know, high level stuff.<\/p>\n
Yuval: That\u2019s wonderful. Bill, thank you so much for joining me today.<\/p>\n
Bill: Yuval, it\u2019s a pleasure and this is a great experience. Thank you very much.<\/p>\n","protected":false},"excerpt":{"rendered":"Bill Wisotsky, Principal Quantum Systems Architect at SAS, joins Yuval to discuss SAS\u2019s vendor-agnostic, hybrid approach to quantum…\n","protected":false},"author":2,"featured_media":181810,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[111,139,69,115480,107856,145],"class_list":["post-181809","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-new-zealand","tag-newzealand","tag-nz","tag-quantum-systems-architect","tag-sas","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/181809","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/comments?post=181809"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/181809\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media\/181810"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media?parent=181809"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/categories?post=181809"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/tags?post=181809"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}