Tommaso Demarie: Quantum Computing Startup, PHD Academia to Founder & Michelin Mistake Ownership - E435

Tommaso Demarie, CEO & Cofounder of Entropica Labs, and Jeremy Au talked about three main themes:

1. Quantum Computing Startup: Tommaso shared his insights on quantum computing and its potential applications. He talked about his company, Entropica Labs, which focuses on developing software for quantum error correction, a crucial element in quantum computing that mitigates errors caused by noise. He explained that quantum computers, unlike classical computers, leverage quantum mechanics to process information, enabling them to tackle complex problems that classical computers cannot solve efficiently. He also emphasized the transformative potential in fields like secure communications, chemistry, and optimization problems, providing examples such as the ability to crack cryptographic codes and simulate chemical properties of molecules more accurately​​.

2. PHD Academia to Founder: Tommaso first pursued a bachelor's and a master's in physics, followed by a PhD in quantum information theory. Despite an initial job in financial risk modeling, his passion for quantum computing led him to join the academic research community in Singapore. He shared how IBM's 2016 release of the first cloud-based quantum computer, making quantum computing more accessible, motivated Tommaso to transition from academia to entrepreneurship. He highlighted the challenges, including the importance of focusing on relevant problems and leveraging one's core competencies​​.

3. Michelin Mistake Ownership: Tommaso shared a personal story about working in a Michelin-starred restaurant, which taught him valuable lessons in ownership and accountability. In this high-pressure environment, he learned the importance of owning mistakes and seeking help to correct them. This experience paralleled his journey as a startup founder, where resilience, continuous learning, and adaptability are crucial. He also emphasized that taking ownership and being genuine in addressing mistakes are key traits for successful leadership and personal growth, especially in the startup ecosystem​​.

Jeremy and Tommaso also talked about revolutionizing climate modeling, the challenges in fundraising for deep science startups, and the evolution of quantum error correction techniques.

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(02:10) Jeremy Au:

Hey, Tommaso. Really excited to have you on the show.

(02:12) Tommaso Demarie:

Hey, Jeremy, it's great to see you.

(02:14) Jeremy Au:

Yeah. I'm glad we finally managed to make this work. I can't wait because you are doing something that I don't understand still, which is all the stuff quantum, like quantum computing. I heard that Antman, the movie went into the quantum universe. I'm sure that was a hundred percent scientifically logical. And so happy to have you on the show. Can you share a little bit about yourself?

(02:34) Tommaso Demarie:

Absolutely, also very happy to be on the show. So I am originally from Italy. I moved to Singapore more than 10 years ago. I'm a physicist by education. I am a geek at heart. And today my role is to be the CEO and the Cofounder of Entropica Labs. As you mentioned, as you hinted, we are a quantum computing company based here in Singapore, six years old already, and we are building a very key component of the software stack for quantum computing, which is called quantum error correction. Something that I would love to tell you about.

(03:04) Jeremy Au:

I'm sure we're going to get into it. Can you share a little bit more before we kind of like go into history and journey, could you share a little bit more about what Entropica does?

(03:11) Tommaso Demarie:

Yeah, of course. So as I mentioned, we are a software company, which means that we don't build quantum computers. But what we do, we tackle one of the fundamental problems related to, to quantum. As the name suggests, quantum, quantum computers are devices that process information using quantum systems and tapping on the very special effects of quantum mechanics.

(03:33) Tommaso Demarie:

So quantum mechanics is fantastic and quantum systems are incredible. But as you can easily imagine, when you're trying to handle atoms or photons or very small particles in general. The problem is that these things interact with absolutely everything. They always interact with the universe, they always interact with nature, and the big issue that quantum computers have is something called noise.

Not noise, like they're very noisy, but noise in the sense that you have interactions with absolutely everything around them. And there is a problem because if you try to run a quantum computation on a quantum computer, very quickly errors accumulate and you get nothing valuable out of it. So we need a solution and the solution is called quantum error correction, which is a set of procedures that you need to apply on a quantum computer to make sure that you can correct errors faster than they accumulate in the computation.

What we're doing is building the software stack that you need to perform error correction on quantum computers. And I try to give you a little bit of analogy. I love analogies, especially in physics. They're usually very effective. You may be familiar with one of the best examples of error correction in our everyday life, which is a refrigerator that you most likely have in your kitchen.

A refrigerator is an error correction device. Because what it does, it removes entropy, and sorry if I'm being a bit maybe too academic here, but it removes entropy from the food that you have. Because if you just leave the food outside, especially in a place like Singapore, it's going to go bad very, very quickly. And you can think of that as an error happening with your food. If you put it inside the fridge, what the fridge does is pumping entropy out quite literally. Actually, this is why you have a heat pump behind the fridge, cools down the food, preserves the food and maintains everything as it should for, much longer time than it would otherwise.

In a way, quantum error correction does something metaphorically very similar. It pumps entropy out of a quantum computer so that you can preserve the state of your computation for longer time. And by the way, it is very elaborate description that I've just given you is also part of the reason why the name of the company is Entropica because entropy is such a key concept in everything that we are discussing.

(05:25) Jeremy Au:

Amazing. And going back in time first get into this because, you are a bachelor's in physics and so far off. So how has that first love for science and quantum computing come about?

(05:36) Tommaso Demarie:

Okay. If you go, or if I go really back in time, I remember when I was a kid, I grew up reading a lot of science fiction. I loved it. I grew up loving science, loving technology, loving the concepts of computing, I, I read a lot, all of Herbert's books, Dune and Forward, way before it became a sensation. I love Asimov. I love in the foundation all the robot series books. So there was this natural inclination for everything related to technology that I had. Now, this is the fun part, right? I can also share something very personal that certainly impacted me when I was much younger. What happened when I was 11 actually is that one of my close relatives actually got diagnosed with ALS, which is a fatal motor neuron disease. It's pretty horrible. And I remember very vividly this point that was made multiple times, right? That the doctors would say, there is nothing we can do. We don't understand enough about the disease that there is absolutely nothing we can do. We can just wait, try to help the person, which is really heartbreaking, right?

And reflecting on this episode, something that became very clear to me is that when we don't know things, then pain happens. Suffering happens. And you can turn that concept and the positive view is that the more we know, the more we learn about the universe, the more we learn about nature. And the more I believe we can fix many of the problems and much of the sufferings the still affect humanity, the planet animals. And this is something that I hold very dearly. The reason why when I always wanted to be a scientist is because I think there is something fundamentally important about learning. We learn about nature, we learn about physics, we can build better technology, and we can improve everyone's life.

And I strongly believe in that. And this is why I went to study physics. As you mentioned, I, I I first did a bachelor in physics. Then I did a master in environmental physics because actually, I wanted to work on climate models. I wanted to work on mathematical models for the climate. And this is when the whole idea of quantum computing came to be, came to be because with my professor at the time, we appreciated that some of the computations, in fact, most of the computations in, in climate models are incredibly hard. So even if you use supercomputers, there is a limit to what you can do.

And then we started to learn about quantum computing, which was still quite a new idea. It was a lifetime ago. This was in 2008. So it was 16 years ago. It was still quite a novel idea in, in, in. In academia.

And we came to appreciate that there are a lot of problems out there that require massive computing power. And the whole promise of quantum computing is that they can solve certain problems that are unsolvable by the traditional computers that we have, even by supercomputers. So the connection was very neat. Look at large problems in environmental physics and learn that some problems need more. And this is when I became really excited and a little bit obsessed with the whole concept of quantum computing.

(08:19) Jeremy Au:

Amazing. When you look at that, you decided to do a PhD, right? So what was that rationale behind the PhD? Because, someone can love quantum computing, but maybe not do a PhD, although I feel like you have to be a PhD to understand quantum computing. But what was that rationale behind the PhD?

(08:33) Tommaso Demarie:

Sure. So fun fact, I didn't go to do a PhD straight away. In fact, I got offered a job by a bank in Italy. I was still in Italy at the time. They offered me a job in the risk management department of the bank, which I must say was actually quite a lot of fun because we were working with statistical models, mathematical models, Monte Carlo methods to, to evaluate risk across multiple operations of the bank.

I loved it. And it was a great experience. It lasted for almost a year that gave me a bit more of I guess, practical understanding and practical thinking, right? Become a bit more pragmatic. But to answer your question, the reason why I still eventually applied for a PhD scholarship and got the scholarship and moved to Australia of all places. So I moved to Sydney in 2010 to do a PhD. It's because at the time if you wanted to work on quantum computing, the reality is that you didn't have many avenues to do that. The only proper journey that you could undertake was the academic one especially in Italy. I mean, if I wanted to work on quantum computing in Italy, that would have not happened, but in bigger academic centers globally, they had opportunities for it.

So there wasn't really much of a choice, but also I personally love learning. I really wanted to move out of Europe. I wanted to go to Australia. I wanted to face the challenge of being in a completely different place where I don't know anyone, where I am on my own and I have to build my own future, my own career. And I was very lucky to get accepted into a group that had really incredible professors, working on different aspects of what is called quantum information theory. So when the opportunity arise, I took it. I thank everyone at the bank, quit my job, pack my bag. And once I got the visa, I moved to Australia. It was really the only choice. To emphasize the point, it was really the only choice if you wanted to work on quantum computing.

But now I want to break one of the myths. It's not a big myth, but it's something that you said. You said that you need a PhD to work on quantum computing. In 2010, that would have been true. In 2024, that is not true anymore. And I can speak out of experience because some of the best folks that we have in Entropica, they don't have a PhD in quantum computing. They're very smart. They still have a strong academic background, but some of them have a bachelor, some of them have a master. What is really beautiful about what has changed in the whole field of quantum computing in the last 14, 15 years is that today, you can play a really big role without the need to have the heavy academic background that, for example, I have because there are so many opportunities, so many open problems across software, artificial intelligence, machine learning, architectural design, and quantum information.

That in fact, one of the big goals that we have is to attract people across different domains to really come and contributing to the development of the field. So I start to make a point here and the point is that there is a lot of work to do in quantum computing, some of the most exciting work that you most likely will do in your life, especially because the field is new, is starting, and you don't need a PhD anymore. There's a lot of open problems that don't require a PhD. They require hard work. They require good understanding of computing, but they don't require a PhD.

(11:22) Jeremy Au:

And what's interesting is that, the classic point of view for our PhDs is that they're going to become a professor, like join academia and you decided to become a founder instead. Could you share a little bit more about that career choice?

(11:34) Tommaso Demarie:

Yes, I can. After the PhD, I moved to Singapore. In Singapore, we are very lucky because we have one of the best centers of excellence for research on quantum technologies. It is the Center for Quantum Technologies, the CQT, is based within NUS and in 2014, when I moved to Singapore, I was affiliated with the CQT. Although my position was being a research postdoc, research fellow at SUTD, Singapore University of Technology and Design. And I was very lucky to join the group of Joe Fitzsimons, who for the records also transitioned from academia to entrepreneurship, and today is the CEO and founder of Horizon Quantum, another quantum computing company based in Singapore.

(12:11) Tommaso Demarie:

I was very lucky to join his group. And the reason why I'm saying that is because within that group, we all had a very pragmatic view of the field of quantum computing. We were doing theoretical research. So we were working on algorithms on cryptographic protocols and the likes, but the goal was not so much to pursue epistemological outcomes, which means just create knowledge for the sake of knowledge. The goal was always to make quantum computing a practical technology. And that has really helped me to shape the understanding that at the end of the day, if you want to create impact at a very large scale in today's world, it's unlikely that you can do that in academia.

And in fact, in today's world, we also have so many opportunities, especially when it comes to company building and entrepreneurship. The tools and the resources available to people are just unbelievable, even compared to 10 or 15 years ago. So the first part of the answer is if you want to create large scale impact and you're really passionate about making the technology real, I believe that today you have to do it outside of academia.

Then there is a personal reason, right? And the personal reason is that I believe technology is fundamentally a force for good. And I didn't want quantum computing to remain a theoretical endeavor or to remain a conceptual endeavor. I wanted to transition it. I wanted to contribute to transitioning it outside of academic halls and make sure that it could benefit everyone, just like computers are benefiting everyone. And the third point is that while I was a postdoc, there was a huge shift in the field. And this is more about timing, about the right timing. So in 2016, IBM put the first quantum computer on the cloud. It was a very small prototype.

It was a five qubit device. A qubit is a quantum bit. So think of it as a five quantum bit processor, but it was available to everyone for free. You just needed to connect API call and you could submit small, simple quantum computations to this device and get a result back. And you must appreciate how incredible it was because before that device was available online, if you as a theoretical researcher wanted to test something on a real quantum processor, you had to find an experimental group. The head machine, the machine needed to be available. You needed to come up with a proposal. You needed to spend a lot of time building up and all of that. Maybe spend one year until the right time was available to run the experiment, run the experiment, analyze the data. I mean, it was what it was, right? But as you can appreciate, it took a lot of time to get anything done. Now the device is available to you. You just go connect in five minutes, your result. And it's quite funny because I think at the very beginning, I was one of the heaviest user. I cannot prove this claim but I make the claim nonetheless.

(14:40) Tommaso Demarie:

Because of a project we were doing, I was one of the heaviest users of that machine. I was submitting thousands of requests to those early devices. So why am I saying that is because there was a big shift. All of a sudden, quantum computing is not just an idea, it's not just a theoretical effort, it's not just an academic effort, but there is a big company putting the machine available for you to use. I loved it. Impact, personal motivation, and good timing. When you put all of that together, 2016, I really seriously started to ask myself, Do I want to try to become a professor? Or do I want to be real to my beliefs, to my values, to my passion, and cross the chasm and try to make it as an entrepreneur.

(15:17) Jeremy Au:

Amazing. And, from that perspective, you decided to become a founder and a lot of PhDs and scientists often may want to make a transition towards being a founder. What was your experience in deciding to build that first startup?

(15:29) Tommaso Demarie:

It's the most incredibly painful thing you can do to yourself. It is a pain from the beginning to the end. It's amazing though, especially in the first few years, we. I say we because I wasn't alone. And my advice is if you can, don't do it alone. Find somebody you really trust, somebody who shares your values, who shares your mission, your vision, and doing it together because it is a lonely job. It is a hard job, and if you have somebody at your side, it really changes everything. So together with Ewan, who's our CTO and Cofounder at Entropica. In 2018, we both quit our jobs at university. And what we did was to join Entrepreneur First, at the time EF at a branch here in Singapore.

So we joined as a team from the very beginning, and this was the start of our journey. But what I was going to say is that in the first few years, we committed all the mistakes that you can imagine as a founder, which is incredibly frustrating. But it is also a great learning experience, especially when you come from academia, the reality is that there is not much else you can do, but keep hitting your nose against the wall until you learn what the door is and you stop hitting your nose against the wall. In fact, I can tell you a bit more, right? I can tell you, I can tell you how we started Entropica and one of the first big quote unquote mistakes we commit.

There is a tendency in entrepreneurship, or especially in startup building, to come up with a very strong problem statement from the very beginning. We are building X to solve Y so we can do Z. And there is sometimes an emphasis, especially from investors or from people around you, to push you to craft things in that way, which makes sense. It works very well for 90%, maybe 95 percent of companies. But sometimes it doesn't work. you And at the very beginning for us, we, I believe today we needed to take a different approach, but we still followed the advice. And what we did was to say, we are going to solve hard problems in computational biology using quantum computing.

Why computational biology? Because we founders both love it. I personally love it. If I could go back in time, if I could live another life, I would love to do a PhD in neuroscience. I love it. Put that aside. A lot of interesting problems, high impact. If you can improve our understanding of genomics, for example, we can really start seriously tackling some of the hardest diseases that still exist, that still exist today.

As I mentioned, hard computational problems saw very fertile ground for quantum computing to have an impact. The problem with all of that is that neither Ewan nor I are biologists. And it is incredibly difficult to find an entry point to a field which is already very complex and you don't fully understand. So what happened at the beginning of the life of Entropica is that we spent about a year trying to find the first problem statement that would make sense for us to tackle. And you see that we were getting the logic, we were getting the rational wrong. And we broke the impulse when we look at each other and we said, look, this is not going to work.

We are experts in quantum computing. We understand quantum computing incredibly well. We understand the problems in quantum computing. Let's not overcomplicate things. Let's take a step back. Let's identify what is missing in quantum computing and work on that. And that shift was incredible because in literally, we went from, we've been fundraising for more than 12 months and nobody is believing in our vision to in less than three weeks, we got a term sheet. But why? Because we were focusing on our strengths. We were focusing on the problems that we understand. So that was one of the first lessons I learned. Don't ever overcomplicate things. Focus on what you know. Use your skills. Use your understanding. Use that as a starting point. Also, don't be scared of mistakes. Commit as many mistakes as you can afford to commit, because that is the best way to learn. Really, that is the best way to learn. So I wrap up this lengthy answer to say that The experience is incredibly painful and it feels like you're just always, always falling down, hitting your head, but you know what is fine. You raise and you keep walking and running again until you fall one more time and that's okay. That's part of the process.

(19:16) Jeremy Au:

What is something that you discovered about building a startup that you didn't really understand as a PhD or scientist.

(19:24) Tommaso Demarie:

It was a good question.

Okay, let me, let me try to, let me try to answer that by mapping the similarities and the differences between doing science and building a startup or building a company. And I'll, and I'll use the example I just made as a, as a starting point. I think in both cases, the starting point is very similar. What you should do, I believe, you should fall in love with a problem. With a problem that you understand that you can articulate.

(19:47) Tommaso Demarie:

Something I learned is that it's very easy to fall in love with the solution when you build a company. Because you think of the solution, you think of the product, you think of the software, you think of whatever you're building as your little baby that you're protecting from the world, from the environment, and you want this baby to grow. And it's a very human thing to do. But if you fall in love with the solution, you'll become very rigid. You'll become very tight. And most likely things are not going to work. You should fall in love with the problem. The thing that should always be clear in your mind is the problem. The solution will change a million times. The problem is the key. And this is true in science, like it is true in entrepreneurship. You want to have a crystal clear understanding of the problem, and then build a solution for that. What changes is the validation process and the outcomes that you seek.

So in science, you have a very rigorous validation process, the scientific method. You can't sway away from it. There are steps you need to follow. You make a hypothesis. You build an experiment, you test it, you elaborate the data, and then you proceed. But in a startup, you have much more flexibility. You should be very pragmatic. I think one mistake we committed multiple times during the life of Entropica is that we were confusing these two methods.

Sometimes we were too rigid. We were too strict with ourselves. In startups is a little bit like you fix. Imagine you're on a boat, right? And you fix a distant star and the star, then north star is your goal is, is the problem you want to solve. And you start sailing. And at the beginning, there's only two of you and the boat is full of holes is constantly sinking. And you're there with a bucket trying to, throw the water out of the boat. But the more you proceed, the more people join the boat, the more money you have, the more partners you have. So you can grow the boat, you can become bigger. But the important point here is that. You don't need to sail through the storm.

Like you have the ability to decide the path. What matters is the North Star, but the path between today and the North Star is up to you. And this is quite different from the scientific method, where you need to follow a very rigorous approach. And I think that is very, very important. Don't, don't confuse the two. Don't try to be too rigid. Be flexible, be pragmatic, keep in mind that you are falling in love with the problem, not with the solution. Seek the North Star, but navigate through the storms. Sometimes you have to go through the storm and then make sure that you survive and get out of it. And also the end goals are very different.

In science, it's completely valid that your end goal is pure knowledge. What you're doing, you're increasing knowledge humanity's knowledge, even if it doesn't have any practical application. You increase the understanding of nature and there's a very noble goal. But if you're building a company, the goal has to be different.

Eventually you want to deliver value. You want to be profitable. You need to get back to your stakeholders, to your investors with financial returns, and I think it's also important for scientists who turn entrepreneurs to keep that distinction very clear in their minds. And I'm not trying to say that money is the only thing that matters, although money does matter. What I'm trying to say also is that you are seeking practical outcomes. For us, we want to make quantum computers accessible to developers. We want to make quantum computers valuable. We want to make sure that quantum computers can solve those hard problems and benefit all of humanity. And that is the goal.

And obviously you're also seeking financial growth and financial profitability out of that. I would say this is really the biggest lesson at the high level for me. The difference between the scientific approach and let's call it the startup approach. It is very, it's very beautiful. Similar starting point, different end goals and different trajectories. And it's important to keep that in mind.

(23:03) Jeremy Au:

What's interesting is that now you're building this company in quantum computing especially with a software perspective. What does quantum computing do for us that traditional computer can't do? I know this sounds like a basic question, but you know, my computer can already do zoom calls, run Excel, run a risk management model, generate blockchain. So, what does a quantum software do that gives us some advantages and what are some applications as a result?

(23:29) Tommaso Demarie:

Okay, take all the examples you just listed, your computer will continue to do all of that. So quantum computing is not going to replace your zoom call. We should think about the two paradigms in a slightly different way. And first of all, I would say one thing, classical or conventional computers, usually in the field, we refer to them as classical computers. The one that we are using right now, for example, but also your phone or an HPC. These are all classical computers.

A GPU is based on classical computing logic as well, or FPGA. These are all examples of classical computing systems. And those devices are incredibly powerful. Incredibly powerful. I mean, in the last decade, we learned how to parallelize things. We learned how to speed up linear algebra, how to speed up tensor calculations.

And the result of all of those learnings is ChatGPT, better AI models and so forth. So conventional computers, classical computers are incredibly powerful, and yet, there are some problems that have a complexity so high, that even if you were to put all of the computing power that we have available on the planet today, you wouldn't be able to solve them.

I'll give you a simple example. When you connect to your Gmail account or to any email provider account, or when you connect to your bank, what you're doing, you're establishing a secure communication channel between you and the such that when you send your password, even if somebody is eavesdropping, they are not able to read your passport.

So you create a secure communication channel between the two parties and you can exchange information securely across. You can look at your bank account details. You can do all of that and you know that you're safe. Nobody can spy on you. And the reason why you are safe is because when you create this communication channel, what you're really doing is you're using the complexity of a problem to protect you. The problem is that if I take two very large prime numbers and I multiply them together which is a very simple thing to do, you can do it by hand, right? You get a very big number. So this is easy to do. But if I give you the very big number and I ask you to find the two prime factors The multiply together will give you that big number. It turns out that this problem is impossible to solve, at a certain scale, for traditional computers. It will take them millions of years to crack them, to crack this problem. And because of that, you can use this property in a very smart way. You can build secure communication channels. So this is a simple example that we use every day, even if we're not aware of it. even if we're not aware of it. of a hard computational problem that even given all the GPU power that we have available today, you will not be able to crack. And because of this guarantee, we can use the internet securely. Now, what happens is that quantum computers, it turns out, can actually crack the problem efficiently.

So instead of taking millions of years to find the prime factors, it might take them a couple of hours or maybe a day. So you can appreciate the difference in timescales. We go from, the time that it took to go from dinosaurs to humans, to a couple of hours. It's completely different orders of magnitude.

And that gives an example of why quantum computers are incredibly powerful. But it also gives an example of a problem that we encounter every day, even if we are not aware of it, that can be cracked with quantum computers. Now, obviously here there is negative connotation and the negative connotation is that quantum computers will impact the field of cryptography, which is something that is a huge discussion happening today.

But put it aside, there are many other similar problems in a number of fields that will benefit from quantum computing power. Linear algebra can be solved faster with quantum computers, which is applications across all of engineering. Certain machine learning techniques can benefit from speedups from quantum computers, which is exciting because the potential of that could be to train more powerful models in less time or using less data and also using less energy because one computers, as far as we can tell today, they are more energy efficient than conventional computers.

Which require a lot of cooling and a lot of power just to, just to function. Then you have applications in chemistry if you want to fully simulate the chemical properties of molecules, you cannot do that with classical computers because molecules are quantum mechanical systems. So you need the quantum mechanical system for that, and a quantum computer is such, which will help and improve our understanding of chemistry and material science, which most likely will have effects across pretty much everything that we do in modern economy. Imagine if you could build more efficient batteries, which will have repercussions in automotive, in transportation. Imagine if you can build more efficient solar panels.

Imagine if you can simulate what happens inside the fusion reactor more effectively. that it can help us get closer to to clean energy faster. Thanks to fusion. This is just to give you a flavor of the kind of very hard computational problems that exist in nature that today we struggle to tackle, but that quantum computing could help us solve. And if quantum computers will succeed in solving even some of these problems. I believe we will enter a new era of technological innovation, scientific discoveries, something very similar to what happened a hundred years ago with the understanding of better physical models, which gave us the computer and then everything changed for human societies. It's incredibly exciting, I hope I managed to answer your question.

(28:26) Jeremy Au:

Amazing. Could you share about time that you personally have been brave?

(28:29) Tommaso Demarie:

I can. This will be a small story. There's not going to be a grand story of bravery or anything like that, but it's a story that has a meaning to me. And, it's a cute little thing that I still remember. So I'll tell you three things about me that have nothing to do with physics, nothing to do with quantum mechanics, nothing to do with entrepreneurship. One is that I'm a sommelier. I love, I love food. I love wine and during my master's, in the evenings, I actually went to sommelier school in Italy, and throughout the course of the year, I, I got my certification. Fun fact, I also worked in a Michelin star restaurant, which is also something I did during my studies. So at night I was going to work until very late as a comi, which is really the lowest rank in the hierarchy of a kitchen. It was an amazing experience. It was off the books, but the restaurant very kindly let me do that. And this is related to the story that I'm going to share with you in a second.

And the last fun point, just for people to know. So I make myself a little bit more human. I love animals and I have a pet parrot at home. It gives me a lot of joy every day. Let me tell you about the story.

So when I was working at the Michelin star restaurant, I pretty much spent the first week cutting vegetables, which is not really the more, how can I say, glamorous part of the job, but, you need to start somewhere. So after a week of chopping carrots, onion, and garlic, and parsley, finally they gave me this little task. And the task was very simple. I had to help prepare the amuse bouche for the evening. So what happens is that usually there is a fixed menu and when you sit down, they will bring you a little appetizer, a little something just to start the meal. Very simple and no big deal. It's called an amuse bouche in case people are not familiar with that.

You also need to imagine that inside one of these kitchens, most of what happens is that people yell at each other all the time. It is high pressure. Everyone is very tense and stressed. There is a lot of yelling. It feels a little bit like being in the military when you're doing your NS. There's always somebody yelling at you for no reason.

Anyway, after a week of chopping veggies, they tell me, go and prepare the amuse bouche. All you have to do is to fill the plate with a little bit of this sauce. That's it. They don't show me how to do it. Off I go, I start preparing the plates, and I put as much sauce as I think would make sense. And the plates go out. Five minutes later, the chef, who usually at this level is not working in the kitchen anymore, but sits outside and entertains customers, The chef storms into the kitchen through the door and starts yelling like he's gonna kill somebody. Screaming, who prepared the amuse bouche? Who put so much sauce? Who is that idiot? I'm gonna strangle him, blah, blah, blah, blah, blah. Everyone freezes.

What they told me before was that, we might yell, we might look angry, and it is certainly a very stressful environment, but if you make a mistake, man, own it. So throughout all the yelling, the chef stops, being vain, pumping on his neck. I raise my hand and I say, chef, I'm sorry. I did it. And I was expecting that he would take me, literally grab me on the kitchen attire and throw me out of the kitchen from the back door. Comes to me and stares at me. It was a big guy. It was taller than me and very big. And he said, all right, I'll show you how to do it. Make sure not to commit the same mistake again.

He didn't use his exact words, but you get the idea. And he calmed down. Goes, shows me how to do it. I learn and I don't mess it up again. And it was very interesting because after that, the chef du cuisine, who is basically the second in command, comes to me, takes me aside and says, look, I made a mistake.

Fine. But well done. Very few people own it. It's very easy in kitchens, especially when somebody is yelling for people to disappear and to hide or to pretend nothing happened. You own it. That's the way to do it. Good on you. He also said, if you F it up again, I'm going to punch you. But that's fair. It's a very small story, but I think it told me something very important that even in high stress situations, even when things look very ugly and very nasty, the best thing that you can do is just to own your mistakes.

If you did it, say it, explain why you did it, ask for help, ask how you can avoid doing it again, show that you're genuine about it. And usually people react positively to that. And I think this is true in pretty much everything you do, but it's particularly true in entrepreneurship and startups because you will commit so many mistakes that the earlier you learn this lesson, the happier you will be. Also more successful you're going to be.

(32:18) Jeremy Au:

Yeah. Amazing. On that note, I'd love to kind of like summarize the three big takeaways I captured my notes here. First is, thank you so much for sharing about your passion and how you came to love quantum physics and quantum computing. That was just fascinating to hear how you took those initial steps away into word science and then away from, financial risk modeling into quantum computing again.

Secondly, thanks so much for sharing about your reflections on what it takes to make the move from being a scientist and researcher into being a founder of a startup, and it was fantastic to hear the mistakes that you made and also the insights that you had about making sure to be realistic and pragmatic and self aligned in how to tackle the problem statement, especially in a highly complex field as, still emerging. Lastly, thanks so much for sharing about your story of courage, about what it takes to take ownership. I thought it was fantastic to hear about how you think about taking ownership in the context of the restaurant where you were at, but also in the context of how to approach solving for the space of quantum computing as a startup founder.

On that note, thank you so much for sharing.

(33:22) Tommaso Demarie:

Thanks, Jeremy. It was great. Really good to see you. Thank you.