Making the Business Case for Quantum Computing
Overview
In this episode of The Quantum Spin by HKA, host Veronica Combs talks with Elena Yndurain, a professor, business leader and author. She teaches at IE Business School in Madrid, Spain and specializes in the practical application of theory in Quantum, Artificial Intelligence, SaaS, and more. She created the first Quantum Computing curriculum for executives. In her new book, she analyzed four use cases in 11 industries. She and Veronica discuss working for startups, moving beyond early adopters, and making relevant use cases for quantum technology. Here is the link to her new book: Quantum Computing Strategy: Foundations and Applicability
00:00 Introduction to Quantum Spin Podcast
00:35 Guest Introduction: Elena Yndurain
01:01 Early Encounters with Quantum Computing
02:39 Quantum Technology Readiness
03:35 Challenges in Quantum Computing
04:53 Quantum Computing Metrics and Benchmarks
08:26 Big Tech vs. Startups in Quantum
12:12 Challenges for Quantum Startups
15:47 Writing a Quantum Computing Book
20:07 Use Cases and Applications of Quantum Computing
24:30 Characteristics for Adopting New Technologies
28:02 Conclusion and Farewell
Transcript
[00:00:00] Veronica: Hello, and welcome to the quantum spin by HKA. I’m Veronica Combs. I’m a writer and an editor here at the agency. I get to talk every day with really smart people working on really fascinating subjects, everything in the Quantum industry, from hardware to software. On our podcast, we focus in on quantum communication, and by that I don’t mean making networks safe from hacking or entangling photons over long distance, but talking about the technology.
[00:00:26] How do you explain these complicated concepts to people who don’t have a background in science and engineering but want to understand all the same?
[00:00:35] Today, I’m speaking with Elena Yndurain, who is a technology professor at the IE business school. She has worked for big corporations and startup companies in the quantum industry. And right now she is about to publish her first book.
[00:00:50] It’s called quantum computing strategy foundations and applicability. So that’s for a good first read for the new year. Thanks for joining us today, Elena. Thank you very much for having me.
[00:00:59] Elena: It’s a pleasure to be here.
[00:01:01] Veronica: We always say that HKA has been around as one of the first press public relations companies to cover the quantum industry, but your first encounter with a quantum computer was way back in 2000,
[00:01:12] Elena: So my first experience was kind of by chance. I went to a conference by Ignacio Sirac. He’s a theoretical physicist.
[00:01:20] And at the time he was talking about a theoretical framework on how to build a quantum computer. He defined it through a special type of qubit technology that are trapped ions. And he showed the path to build a quantum computer, what you could do with a quantum computer. But at that stage, this was a theoretical framework.
[00:01:39] It wasn’t really a, you know, a real practical quantum computer. And it wasn’t until 2016 that IBM opened up the first quantum computer to the world. And that was a very tiny quantum computer, but that I think was a huge milestone because that showed that the technology was feasible, right?
[00:01:58] And it opened it up for anyone to test it. And that’s when I got into quantum computing because I had been working and launching new technologies into the market in different tech corporations. And also Industry companies, when IBM opened the computer, I realized I was like, Oh, this is coming.
[00:02:13] And I remembered the talk that I attended, you know, with Ignacio, although it was a physics talk, we did have someone in the audience from a bank, from a big bank in Spain, actually asking questions about encryption. I was very intrigued by the time, but frankly,
[00:02:26] I didn’t understand a word of what he said, right? Because it was physics. And although my background, it’s in science, I’m a mathematician computer scientist, I had no idea about physics or theoretical physics.
[00:02:37] It’s 20, 25 years forward, what we see, if we think about the technology readiness. At the time when I heard Ignacio, he was talking about the framework, which is the first level of technology readiness. But now we’re at a level that we’re actually testing the technology. We have some proof of concepts, so it’s shown that the technology works.
[00:02:57] However, we need to scale it. We need to improve the fidelity of the technology. These qubits are the heart of the quantum computer. They’re quantum bits. They are made of different technologies.
[00:03:07] Ignacio spoke about one, which is trapped ions. But they have to maintain the physics, the quantum mechanics properties. Qubits are based on quantum mechanics and they have a property that needs to be maintained. If it doesn’t, if it’s not maintained, then you have errors. And where we are right now is that we’re trying to get to increase the fidelity to have less errors.
[00:03:28] And if we do that, we can transition from this proof of concept to a real application that has some business value. And this is where we are right now. How do we scale this technology architecture? If you want to know a bit more about where is the technology heading, you can read this in my book.
[00:03:43] Veronica: Yes. 2000 was really sort of the start of the consumer Internet . And it’s interesting to think about your 1st experience with that discussion of a framework. Now there are companies using trapped ion architectures to run algorithms and test optimization problems and in some ways, 25 years seems like the blink of an eye and other times, it seems like quite a long time. And there was no, we were not thinking about X, or Facebook, or LinkedIn, or Netflix, or ordering our groceries over the Internet to be delivered to our home.
[00:04:14] It is really interesting to compare the 2 technologies and the digital transformation that we’ve seen in so many industries in the last 25 years.
[00:04:22] Elena: As I was mentioning, we have that technology readiness, you know, level, it goes through different phases. So this is nothing new. We’ve seen many technologies evolving. I mean, you were talking about the year 2000, there was a huge internet bubble, right?
[00:04:35] So you have from a technology point of view, you have to go through different stages to evolve a technology, then there’s also a market bubble that can happen and then things explode. I think that it’s also interesting that there’s no, not even like a clear metric to actually measure where’s the technology today.
[00:04:53] If you think about these errors that I mentioned, you have the qubits, they decohere, which is when they lose their properties, and we don’t really know, even the the chip that Google you know, Willow, which is a great chip, they showed a real example. But we don’t really know what is the metric, what is the quality of that qubit.
[00:05:08] So it’s not about the number of qubits. At the beginning, I remember, at the early 2016 to 2020, I would say all the companies were talking about qubits we have, then they try to come up with some metrics. They call it different metrics, quantum volume algorithmic qubits.
[00:05:23] So we’re trying to find some type of formula to help people understand. What is the benchmark for my quantum computer? How well does it work? But the reality is that there’s no common benchmark so that we can’t really know how well the computer works. Right. So even if a company comes out tomorrow with a different qubit count, which usually by the way, these qubits, they’re not really physical qubits, they’re logical qubits.
[00:05:46] So what they do is they group, you know, they have techniques and grouping qubits to compensate for those errors. So that we can really have a quality qubit. But we don’t know what the quality is. That’s my bottom line.
[00:05:57] We don’t know the performance. Basically we know we’re doing tests. We have a lot of proof of concepts. We know we even have a, you know, Google came up with an example to help us understand the potential, but what did it mean with useful?
[00:06:10] What is useful? How do we define useful? Because even if you think about technology already, at the end, you have a commercial quantum computer. Great. But then you have a whole technology adoption, right?
[00:06:21] So at the beginning, you have problems that are practical. And then they start getting more sophisticated. And then at the end, you get like this big majority that you adopt and you start replacing some of the use cases.
[00:06:34] Veronica: Yes, that struck me that that’ll be the new debate, right? 1st, it was what’s quantum advantage and who has it and now it’s what does useful really mean?
[00:06:43] In addition to discussing, with clients or students, how to get to business advantage with quantum technology, I think it’s also a good time to bring up modalities. I think it really is the time to start talking about the strengths and weaknesses of each one. How do you address that when you’re talking about quantum technology?
[00:07:00] Elena: I want to give that a bit of, you know, use that as context is how to explain all the technicalities to everyone, right, or to anyone, I would say because if, you know, you’re right. There are a lot of qubit modalities.
[00:07:13] We don’t really know the difference between one or the other. We don’t know, obviously not, which one works better. And I think there’s no exact answer for that. Right now, what we’re seeing is that, you know, sometimes one qubit modality is better for one specific problem. It’s a kind of case by case approach.
[00:07:31] Which makes it really hard to manage, right? So if you think about that, if you’re a company and you’re thinking about testing quantum. Do you have to make a decision on what qubit modality? Can you test many? Is it worth testing many qubit modalities? You know, that’s not easy.
[00:07:46] And then also how do you access each of those computers? The other question that a lot of companies ask themselves, should I work with a big technology corporation or should I work with a startup? And then to get more complex, should I work with a company that has their own hardware, or should I work with a company that is a software provider, and in a sense, you can consider them as a hub to different platforms, right?
[00:08:09] And then, of course, you have the companies that are platforms like Microsoft or Amazon with their bracket solution. So there are a lot of choices, right? And I think that makes it hard for companies to make decisions, obviously for journalists as well and analysts to keep track of everything.
[00:08:26] Veronica: As I mentioned, you’ve worked for established companies, I think IBM, I think you’ve met Jake Gambetta too, if I remember correctly, but also work for startups. How is that experience different at a big company with lots of resources versus a smaller one working on a few really, really good new ideas.
[00:08:41] Elena: If you are working in a big tech company, like IBM, of course, it’s easier to access clients for starters. You already have those clients, right? You have customers that have bought other technologies that you have. You have a brand and established brand and certain credibility.
[00:08:57] So that, if you talk about quantum, you have that backup, right? And then you have resources, right? You have money. So you have some type of budget to be able to work on quantum and you have people, throughout the company that either they know quantum or if not, they could they could be re scaled.
[00:09:13] So that gives you a big advantage. If you think about a startup, you have to build all that from zero, right? So you need to build that credibility. So you have to establish that. You have to show customers that you can be trusted. To do this, obviously the good part is you can emphasize something that’s unique.
[00:09:31] You can also emphasize your agility, but a big company doesn’t have that agility because they’re bigger, they have a set offering. If you’re in a startup, you can customize many things, right? So that’s one of the benefits. You can also have a niche application, those are the things that you have to, those are the levers that you have to use to be able to, to stand out, to be able to compete with a big player.
[00:09:53] Also you need to rely on your founders, how well can the founders access. These early adopter companies that are willing to test quantum, how well can you access those companies also through your investors? Do you have those connections because it’s not that easy. Obviously you’re going to call a client if you’re a startup and no one knows it’s going to be hard for you to be heard unless the client is an innovator and an early adopter and they want to work with startups, which I’ve also seen that some companies want to work with startups because they’re considering, okay, this is a new technology I know it’s not going to be the good side about having, you know, working with a new technology. That’s not going to work tomorrow. Is it that you can afford testing it with anyone? You don’t have to make a long term commitment, which you would have to make if you work with a big tech corporation, like a three year agreement.
[00:10:41] Elena: Maybe you can do a smaller agreement to do a small test just to, you know, so those are the types of things that are better for a startup that you don’t have in a big tech corporation, but then of course, the biggest problem is budget, so you need, because you don’t have any other source of income, your source of income is this project, so you really need to get the clients on board or the investors and you need to convince the investors that you have a long term plan and you have to deliver on that. So there’s like two sides of the coin, I would say.
[00:11:12] Veronica: Right. I found myself sort of going back and forth between small companies and big companies during my career.
[00:11:17] Do you, have you found the same thing or is there one place you like to be better than another?
[00:11:21] Elena: They’re different, right? One thing I did find out is that, you know, startups, yes, they’re more agile. But some of the problems that I’ve seen in the startup for the same that you have in big corporations.
[00:11:32] Usually, you know, when you think about Attracting talent, retaining talent, hiring, you know, that whole thing. It’s similar, although of course it’s easier to hire someone if you’re in a big corporation like IBM or Microsoft or Nokia that I worked there. It’s easier to motivate someone.
[00:11:48] Although some people do like the startup entrepreneur that you have, you know, the opportunity that’s a bit harder, but at the end of the day. Managing people, it’s the same in one company versus the other. So I was a bit surprised about that. I was kind of expecting that the startup was a bit more family type of style.
[00:12:05] And I found that it was very similar.
[00:12:07] Veronica: I totally agree with all, all of the pros and cons and trade offs wherever you go. What do you see as the biggest challenge facing the CEOs of quantum startups these days? I’m mostly thinking about the smaller companies, not so much the IBMs and Microsoft. What do you think?
[00:12:21] What would be your biggest concern if you were running one of these companies?
[00:12:24] Elena: Yeah, well, the biggest concern, I guess, is keeping up the momentum, right? And customers, so industry corporations mostly, they have been testing quantum computing for a couple of years, they’ve done different tests. I think at this point, it’s safe to say that we are, we have saturated the innovators and the early adopters of this world. Those that are willing to test just to see, because they have either a vision or they just like the technology, but how do you convince, the next category of technology adopters, the pragmatists when you don’t really have, you were mentioning earlier quantum advantage.
[00:12:55] We know the potential quantum advantage, but we don’t really know the real quantum advantage because we don’t have a working quantum computer. So that makes it hard. That’s a big challenge, how do you attract new customers? Or how do you get more use cases in front of your current customers?
[00:13:12] How do you convince them to continue paying for a technology that is definitely not going to be working in three years, and some of them don’t have that long term budget. I’m not even talking about the NVIDIA long term of even, you know, I’m talking even five years from now. Even five years is a challenge for many companies.
[00:13:32] That’s one, and that’s important because you need to get clients unless they become creative, which is what some of them are doing, some of them are expanding, so there’s one thing you can do. You can expand there are different ways to expand. You could expand geographically.
[00:13:47] So for example, I’ve been working with a Japanese startup. I’ve done the whole internationalization to Europe. They’re from Japan. They wanted to go to Europe. They said, okay, Japan is almost saturated. They’re focused on chemistry. We want to go to Europe. So let’s see if we can bring in all the knowledge we have, the clients and the client knowledge, and bring in the research and bring it over into Europe.
[00:14:09] So that’s one. Another is expanding your offering. So we’ve also seen now a lot of companies working with AI now define themselves as quantum and AI because AI now has again come into the spotlight. So that’s another way. And then of course, you could go into another industry. So some companies, I’ve seen a startup that was focused in finance now expanding into chemistry.
[00:14:33] That’s another way. The challenge is keeping the momentum. There are different ways they’re doing right. Of course there is a risk, because going international is hard in terms of money and in terms of the whole operation from an operational point of view, and then of course, hiring people.
[00:14:49] Scaling, it’s another of the challenges, how do you go from small projects? You’re a startup, you’re more flexible. You can charge, even charge less than a big company, maybe make you more interesting. But eventually you need to start charging more. How do you do that?
[00:15:02] I think at this point, a lot of companies need to redefine, do I price by solution? Do I price by project? Because there’s a lot of R& D consulting, but that is not really that easy to maintain, because you need a lot of people to do a consulting project. So a lot of companies are trying to productize their offering, more of a solution, like a software license so they can scale it.
[00:15:21] But that also requires more work, and then it requires that you have to maintain it. So I think that’s the challenge you’re facing, momentum in terms of the market and internally in terms of their growth. How do they grow up and actually have a product that they can maintain?
[00:15:40] Veronica: I hadn’t thought about the early adopters being saturated, but yeah, we are quickly approaching that point.
[00:15:47] So I was just thinking that these pragmatists, like you mentioned, the next phase of potential quantum computing customers, they would really be the target market for your new book because it’s focused on for non physicists.
[00:15:58] And it’s your goal is to demystify the whole thing. The title is “Quantum Computing Strategy Foundations and Applicability”. And I was thinking it would be a good book for me to read as well, just because it sounds very friendly to, like I said, non physicists. It focuses on practical case studies, real world examples.
[00:16:19] How did you get started on this project? It’s a lot of work to write a book.
[00:16:23] Elena: It is. Tell me about it. It’s a lot of work. I’ve been motivated by all the clients that I have spoken to and all my students as well. I’ve been teaching quantum since 2017.
[00:16:34] So when IBM opened quantum computer and then I started studying Quantum well, I realized the learning curve was very steep. I remember I went to visit IBM before I worked there because I was working at a company, at a bank that was a client. So I managed to go and visit the lab, and I met Jay and I met Terry, before they were, you know, big shots.
[00:16:57] And I remember they explained the quantum computer, they showed it to me and they did a really good job, but I understood a bit more than what I had understood with Ignacio, but not a lot more. So I decided to study it. I started studying it and oh my gosh, I found it really hard. And, I had done my PhD in AI, right, in telecommunications engineering, and I found quantum, hard, the concepts were completely different.
[00:17:19] I realized that this was not a migration of technology, that this was rethinking the problem from scratch. You have to rethink the problem from a physics point of view. So that meant you really needed to understand physics. Then, I started teaching quantum for business students.
[00:17:34] I thought that would help me understand it. And I started getting the questions from students. They were like I don’t know what a bit is, why are you talking about a qubit? And I thought, well, you know, they do have a point. Why should they know what a bit is, you know? None of us, as much as we say we understand how computers work, I mean, I studied that in my undergrad, but I don’t remember much, you know, and I haven’t opened a computer in a while to see how it works.
[00:17:56] I started realizing there was a need to explain these concepts in an easier, friendlier way, like you said, so at IBM, I did a lot of that. I did a lot of that with analysts. I would talk about quantum. I gave a lot of talks to clients and I started changing how I explained it. I started thinking a lot about, you know, what are the use cases.
[00:18:16] What are we trying to solve with quantum? What is quantum good for? That was my approach. What I realized when I started thinking about the book was that there was a lot of material out there. So there were a lot of books, very good books, but very technical.
[00:18:29] That scares a lot of people off, because you start reading about all this physics and all this math, and then you’re freaking out or you get scared and then you shut off. A lot of people shut off when they start hearing about superposition entanglement, linear algebra.
[00:18:42] So then we have a lot of industry reports, which are really good that talk about the business value, but they’re a bit, just very much business focused, which, at this point, we don’t really know exactly what the quantum advantage is, so I thought there was a need of having some type of guide in which you would have all the information you need to be able to understand quantum.
[00:19:01] And they should be presented in a way that you could either read it sequentially, or you can just consult it. So maybe you want to consult about a formula, or about a concept, or about a specific use case. And you could just do that, and then I also thought about visuals. So one of the things that I realized in my teaching, is that students react very well to analogies and to visuals.
[00:19:21] So I would always try to give them a visual that had nothing to do with technology, but was easy in a way to understand and to relate to. Then I thought, okay, this book has to have all those things. It has to have analogies, it has to have a lot of examples, real world examples, so you can see, hey, this is the applicability, it has to have all the foundational concepts explained.
[00:19:43] So I actually worked with an illustrator. To create all these visuals.
[00:19:47] Everything is illustrated and I think it makes it easier.
[00:19:49] Veronica: Yes, for sure. That’s always a piece of advice that we give to clients is that you have to have visuals because people can’t, they can’t imagine it. They can’t visualize themselves.
[00:19:59] So you have to, like, you said, show them
[00:20:01] Yeah, I will definitely definitely get a copy of the book for the visuals alone. Nevermind all the good case studies.
[00:20:07] Elena: Yeah.
[00:20:07] Veronica: Are there any case studies that jumped out at you or that you like to use to explain to folks?
[00:20:12] Elena: Sure. So one thing that I also did in the book that I had also done in my previous work was to, with the clients, categorize the use cases, because there’s so many use cases.
[00:20:21] I kind of categorized what are the types of problems you can solve, right? I divided them into four categories. After a lot of conversations with clients, colleagues, et cetera, was to think about one is like the chemical processes, so if you think about how to analyze chemical structures, which, make up materials molecular interactions and all those are present like in drug design and you’d be designing like a fertilizer, you’d be designing a battery, a catalyst for like clean energy, you want to improve a material. So that’s a lot of case studies that are covered for that category and that touch a lot of industries.
[00:21:01] Then another category is optimization. I think that the first practical use case we all heard about was the routing. With D Wave and Volkswagen testing that. So that, if you think about optimization is broader than just routing.
[00:21:14] One is the routing that can be used for transportation. It can be used for network, for telecommunications. It can be used for energy. Which energy source do you use?
[00:21:24] For example, if you think about flights and airports, how do you schedule? How do you do all that changes, you know, that you have to happen in real time?
[00:21:33] Elena: Then the 3rd category is all the probabilistic simulation. So if you think about all the stochastic analysis, so in physics you have a lot of stochastic analysis and this has been used in many fields, right? This field of theoretical physics is used in aerospace, in finance a lot. We think about risk models to analyze the behavior, the flow behavior, for water, for air modeling and prediction systems.
[00:21:53] This has been around, so it’s kind of logical to solve it with a quantum computer. And then, of course, last, but not least, the 4th is artificial intelligence, so you can use it for image recognition.
[00:22:03] So it’s used, for example, for autonomous vehicles for recommendation systems for MRIs. A lot of medical systems, how do you improve the performance of object detection in finance for fraud detection is used a lot. I analyzed it following this structure, I analyzed four use cases per industry, and I have 11 industries.
[00:22:23] So I analyzed a lot of use cases. I learned a lot about use cases that I didn’t even know existed, weather forecasts, for example, caught my attention. And I thought, Oh my gosh, this would be so useful, for so many industries and also for people.
[00:22:37] Because sometimes one of the things that is hard for us to understand with technology is what, what is in it for me as a consumer. We know for business, but in this case, the weather forecast would be good for consumers, obviously. I mean, even in aerospace, the whole like rerouting would be great because we know how horrible it is if you’re waiting for a flight and there’s a problem.
[00:22:57] So if you can reroute that immediately, you think about automotive, how do we get better batteries? If we think about electric vehicles or the aerodynamic design. And even the chemicals, which, you know, for me was like a bit unknown. I mean, yes, we know chemistry is everywhere, but didn’t I didn’t realize that with a quantum.
[00:23:16] This is one of the most promising areas, right? And even the government, they’re doing a lot of tests.
[00:23:21] For satellite imaging, how do we improve satellite imaging? How do you plan military operations?
[00:23:25] Manufacturing as well, and you can plan better demand for the manufacturing process, the assembly line. I mean, I could go on and on, but there are a lot of use cases and what they have in common is how do you improve something that today’s not working? Right? And how could quantum fit? So I think.
[00:23:40] If I were going to talk to a company, I would ask them, what is your problem that you have right now? And then, does this problem fall into one of these categories, right? So that means that quantum would be a good solution for it. And then of course they need to analyze how to approach it.
[00:23:54] Veronica: And I think when you, when you do get into the details of some of our existing systems, like the energy loss, and some of our transmission systems or just the inefficiencies of batteries. I think that really, that helps sometimes for people to grasp the real potential of quantum technology to improve what we have and use less energy or, lose less energy.
[00:24:16] So I think As you said, it’s not a migration of technology, this addition of quantum technology to companies and systems. It’s not a migration. It is something brand new. In your work in the industry with all sorts of companies. Are there characteristics in a person or in a company that help them to adapt new technologies, an openness to innovation or a willingness to fail any markers of that increase the chances of success?
[00:24:44] Elena: I think what’s important is that first of all, these companies are willing to learn they have to have that spirit of wanting to learn, and, because you’re working with the unknown in the sense that you don’t know about physics, but even the physicists that are working on quantum don’t really know, what is the potential of the technology.
[00:25:01] So they know what the theory is, right? But as we’re seeing the technology advance, we don’t really know how well that use case is going to work with the technology. So these companies have to be willing to work with that unknown, to embrace willingness to learn, and what I think they need to do is strategize to make informed decisions. So how do you analyze the use cases? These companies have to have the ability to understand what use cases would benefit from an improvement, you need to really understand the industry, your business, what is it that you’re losing or what is it that you would win, from a business point of view, regardless of the technology.
[00:25:36] And then you have to map that with what is the problem, what problem does this relate to and what would be the potential quantum advantage. And then you need to match that with where’s the technology today. So those are three different angles you need to think about, but if you combine all those three, then you can actually define a roadmap.
[00:25:54] And then obviously these companies need to have a team to work on this, dedicated.
[00:25:59] I’ve seen a lot of companies that have people working part time on quantum. But we just said that quantum is hard to learn. So how can you work part time? If you’ve done all that investment to learn it, why lose it? They need to have the buy in and the money to do this, to test something that they know is not going to be working tomorrow.
[00:26:18] Veronica: So you have both a technical background, you have a degree in engineering and you also have a business background, an MBA. I seem to run into a lot of people in the quantum industry, if they’re good communicators, they seem to have that sort of both sets of skills and that familiarity of switching between maybe one language or another. Is that helpful to communicate quantum if you have those dual backgrounds?
[00:26:40] Elena: Yes, I think it is. So I think it’s helpful to be able to switch, like you said, from one language to the other. If you’re talking to physicists or scientists, you need to be able to talk on their terms. And then if you’re talking to business people, you need to understand. So in both cases, you need to understand what ticks them, right?
[00:26:57] What’s their motivation? And then what do they value? The business people, obviously, they want to see what value does this technology add?
[00:27:03] You need to help them understand the business value, but also educate them on how does it work. Enough for them to make an informed decision and where are we at? And then with the scientists that are actually building the algorithms you need to help them, to give them the security that what they’re doing is actually useful, right?
[00:27:19] And show them the use. It’s like, okay, you’re doing this algorithm that is an enabler, and show them the business part, like, hey, eventually, because of your algorithm, we will be able, for example, you know, in risk assessments to help someone, make more money with a derivative pricing,
[00:27:33] I’ve seen this throughout my career with many technologies, because I’ve launched many technologies into the market. I also have a list of famous last words about, who needs an app, we’re never going to go to the cloud. I mean, I wish I had that in writing, but so it’s true that it’s hard to envision where things are heading to, but I think it helps to understand both sides.
[00:27:52] I mean, even when I wrote the book, I worked with industry experts and I worked with professors, engineers, developers, because I peer reviewed everything.
[00:28:02] Veronica: So your book is “Quantum Computing Strategy, Foundations and Applicability,” and I’ll definitely put it on my to-read list. I appreciate your time today and all your insights. Thank you for joining us.
[00:28:12] Elena: Thank you very much for having me here, Veronica.
Host Veronica Combs is a quantum tech editor, writer and PR professional. She manages public relations for quantum computing and tech clients as an account manager with HKA Marketing Communications, the #1 agency in quantum tech PR. You can find them on X, formerly known as Twitter, @HKA_PR. Veronica joined HKA from TechRepublic, where she was a senior writer. She has covered technology, healthcare and business strategy for more than 10 years. If you’d like to be on the podcast yourself, you can reach her on LinkedIn, Veronica Combs, or you can go to the HKA website and share your suggestion via the Contact Us page.
March 19, 2025
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