SemiWiki.com - Podcast EP320: The Emerging Field of Quantum Technology and the Upcoming Q2B Event with Peter Olcott

Episode Date: December 1, 2025

Daniel is joined by Peter Olcott, Deeptech Principal at First Spark Ventures specializing in early-stage investments. His background encompasses over 20 years of experience in electrical engineering, software engineering, algorithm design, combined hardware-software robotic devices, and novel innovations in biomedical… Read More

Hello, my name is Daniel Nenny, founder of semi-wiki, the open forum for semiconductor professionals. Welcome to the Semiconductor Insiders podcast series. My guest today is Peter Alcott, Deep Tech Principal at First Spark Ventures, specializing in early-stage investments. His background encompasses over 20 years of experience in intellectual engineering, software engineering, algorithm design, combined hardware, software, robotic devices, and novel innovations in biomedical engineering. Peter's academic and professional portfolio consists of more than 150 articles and 34 issued patents spanning various fields such as semiconductor devices, compressed sensing, analog front-end readouts, novel uses of optics, and applications in positron emission tomography,

and radiotherapy. Welcome to the podcast, Peter. Daniel, thanks for having me on. I'm excited to talk to the listeners of SemiWiki. Great. So what brought you to first part ventures? Can you share a little bit of your history? Yeah. So, you know, my undergrad's in computer science,

electrical engineering at UCSD, and kind of got into chip design through a best friend at high school and did chip design for seven years, went through the kind of 2001 semiconductor crash, and then was recruited to, you know, essentially take very fundamental advances in semiconductors and apply them to medicine. So that's what I did at Stanford and then also joined a startup to do that. And so that's my experience. I've done chips, electronics, systems, kind of a generalist and was recruited to an Eric Schmidt-backed DeepTech Fund to invest in Deep Tech across software, Medtech, biotech, and that's kind of where we come today.

So what does First Park Ventures do? Yeah, so we're really a pre-seed seed stage company. So first check in into startups, and most of our founders have deep scientific backgrounds, have been working on problems for 10, sometimes 15 years, and usually make a technical breakthrough. And this breakthrough can be in anywhere from new types of AI software to new therapeutics and biotech or new medical devices or new semiconductors. And we invest and then try to help these companies grow and become successful. So in my career, I've worked with a lot of startups and quite a few of them came out of PhD programs from the university.

I mean, are you still seeing them? Yeah, actually, so I can just kind of go to some of our portfolio companies. Curve Biosciences was straight out of Stanford. Another one, nanolithography, which is doing advanced lithography, was, again, from both the Tech Neon and Stanford. So yeah, I think almost all of our startups have either Stanford, MIT, UC Berkeley, or sometimes all of those things combined as people on the founding team. That's good to hear. So before we talk about quantum to business conference, the conference that we'll be at next month, what brings you to quantum technologies?

What interested you to first? Yeah, I think quantum is really, really hard to analyze because it fundamentally, it's like an exponential scaling technology, which means it can go from nothing to fundamentally changing huge fields, right? obviously this can produce really high returns. So if you're a deep tech venture capitalist, you absolutely need to keep track of quantum because of its massive impact that it can have. And if we look today, it hasn't had massive impact. So we're really kind of at the dawn of this technology. It's very similar to the very first days of semiconductors. For example, when Fairchild,

you know, invented planar lithography, things like that. You know, people probably said back then, you know, what can I do with this semiconductor stuff? And, you know, fast forward 20, 30 years, and it's, it's, you know, touching everything. So that's why as investors, we're definitely interested in quantum technologies. You know, we write about quantum on semi-wiki and we track the readership and it's booming right now. You know, not as big as AI, but it really is. gathering a lot of interest. So that's why I'm spending time here. For the conference that we're attending next month, you're on a panel called Quantum Technologies, Innovation and Investment. Can you

tell us more about that? Yeah. So the reason why I'm moderating the panel is I want, I'm very interested in the field and I want to get the best minds to think, you know, when do these technologies become investable? And what value can these technologies bring. And every six months, that changes. Like, I'll give you a contrarian kind of view, which is AI, as you know, is also exponentially scaling on the semiconductor side. We can see that by the massive increase in the power bills that these data centers are consuming. And one of the questions is, is there anything left for quantum to do? You know, AI is accelerating quickly and taking away many of the applications that quantum was promised to do. And quantum is taking a long time.

So one of my question is, is, you know, what's left? What, what do you, what, you know, people in the field when you talk to customers, what are they super excited about using quantum to solve for them? Right. Yeah, I mean, we've been writing about quantum technologies for quite a few years now, but the big interest is probably this year, and I expect that'll grow in the next few years. But what do you see as the key impact and timeline for quantum production? for quantum production and deployments. From your investments to when will we see it as consumers? Yeah, I think fundamentally, many of these quantum systems are cryogenic or required specialized

technologies and PhDs run. So I don't think you'll ever have one of these in your home, like a personal computer or a laptop, but these will be kind of like the data center. These will be in large labs. And the impact is not direct. So, for example, these will be algorithms to design new drugs. I'll give you an example of one that I saw just last week, which was quantum algorithms can improve something called computational fluid dynamics.

So you're saying, well, what is CFD? Why do I care? Well, CFD can be used to simulate an airplane wing. and an airplane wing is important for commercial airlines. And that at the end of the day, the efficiency of those wings determines the price that you pay to take a plane. So, you know, that's the sort of impact that Quantum has is really at the algorithm stage for companies to solve really hard problems that then lead to better materials, better drugs, or, you know, in this case, better aircraft. Right. Yeah, you know, we've been working on digital twins. So, you know, we're in the semi-connected design world. We're, you know, simulating the CFD and all sorts of multi-physics. And hopefully quantum technologies can make that easier, faster, more accessible. Yeah. So I think that, you know, if you actually bury down into a semiconductor device, they are quantum devices.

Where we see kind of quantum helping is, for example, many of the quantum technologies today are essentially made of like rubidium atoms or cesium atoms or barium atoms. And one of the things is that we would really like to have our quantum technologies made in semiconductor technology because of the massive scalability of companies like TSM. You know, it'd be nice to make your quantum computer on a 300 millimeter wafer. It turns out that it's very, very difficult to make a quantum device in silicon. And we're absolutely going to need, you know, new simulation technologies to make these devices. The biggest bottleneck right now is yield. We can fabricate millions of devices, but most of them don't work. So we definitely need technologies to understand at a very fundamental level, the defects and the physics of trying to make a quantum device in silicon, for example.

Yeah, exactly. So what markets will we see quantum technologies first? I mean, hopefully the medical industry, right? I'll jump there. I'll just go to the first one is cybersecurity. security. And there's kind of a joke to people who track quantum is, you know, if a large transfers from some of these hidden wallets in the blockchain transfer billions of dollars, then you know someone's built a quantum computer. So cryptocurrencies are built on security. And

one of the very first algorithms that was developed, shown to work with quantum, was somebody called Shores algorithm, which can factorize huge integers. So public key cryptography can be cracked by quantum computers. And so right now there are, there's actually, I would say the largest commercial market in quantum is actually making our computers resistant to quantum attack. This is called the quantum secure algorithms. Some of the proposals, for example, would mean that every time you send a request to a website, instead of sending 256 bytes, you need to send 2,000 bytes and you actually need to do a lot of computation. And you can see that this could break a lot of the web. And so there's quite a bit of investment in developing offensive quantum

capabilities to crack encryption and also to make our systems resistant to quantum. On the pharmaceutical side, I would say that that's much more of a research thing. And I think the top application is catalysts. So these catalysts make everything from ammonia, where your food comes from, and a huge amount of electricity and carbon that's emitted in the atmosphere comes from producing ammonia. And quantum can potentially design, for example, a nanocatalyst or some unique structure of atoms that's better than the current industrial catalyst. lists, and this can have huge effects on making fertilizer cheaper, for example, or decarbonizing a process that's very, very, it emits a lot of carbon dioxide, and now you can do a process

that doesn't emit carbon, for example. Good examples. Final question, Peter. Will quantum help unlock new types of AI? That is a really, really good question. And I think this is really groundbreaking research. that's just coming out of Google, and I believe so. And one of the things that a quantum computer can do is model very complicated coupled statistical distributions. Okay, that's super complicated way of saying, for example, if you were to analyze a stock price

and all of the things that affect that stock price from people talking on the internet or people buying and selling stocks, it's a really complicated, thing that then turns into one number, which is the stock price, for example. So how do you represent these sort of complicated statistical distributions is a kind of a general problem? And it turns out that these quantum computers can actually learn these complicated statistical distributions. And then what's very interesting is that when they learn them, they then can generate samples from this

distribution. Okay. And and this is very important because then you can use these samples to train an AI to make decisions based on this. So I think we're just starting to see, you know, this merger of how do you use a quantum computer with state-of-the-art machine learning. Great example. Thank you, Peter. So we will see you at the Q2B conference. It's December 9th through 11th at the Santa Clara Convention Center. Your panel is on the 9th. I will put a link to it in the podcast description. And hopefully we can chat a bit after your panel. Yep. Okay. Well, it's going to great to see you in Santa Clara and looking forward to it. Thank you, Peter. Take care.

That concludes our podcast. Thank you all for listening and have a great day. Thank you.