Hi, everyone. Welcome to the A6NC podcast. I'm Sonal and we're here today to talk more about

quantum computing. And for those of you that want more of like a primer on what it is and how it

works, definitely listen to our other podcast, but you don't have to listen to that other podcast for

this one. The goal today is really talk about what it means to actually build something that's so

cutting edge. I think that's a buzzword that we throw around so lightly. And what we'd like to do in this podcast is actually really like break that down.

And joining us to have that conversation, we have Jeff Cordova, who's the head of software engineering at Raghetti.

And then we also have a Cicincy General Partner Vijay, who's on the board of Raghetti and has a long history actually in the world of high performance computing because you used to do fold at home.

You know, there's been a long history of advances in computer architecture.

You know, the computers that we learned as kids were very straightforward.

But then with high performance, massively parallel machines, like folding at home, we couldn't

just take our algorithms and convert it.

You'd have to really rethink the problem.

When you say highly parallel machines, you literally mean like thousands

and thousands of computers running in parallel next to each other or not necessarily physically

close to each other. In fact, in your case, it was distributed across other people's downtime on

their laptops. So this is like SETI. Yeah, I think SETI came out basically six months before we did.

So this was, for us, it was October of 2000. The project's now been running for almost 20 years. So instead of finding alien world, you guys are focusing on protein

folding. Yeah, exactly. And understanding, especially the intersection of what compute could do in

biology. In that case, they're doing calculations for understanding aspects of biology or protein.

And the interesting thing about that, at least my recollection of it, is that no one

thought that algorithm would work. It didn't look anything like the previous algorithms, except that

it was also doing some kind of chemistry that was interesting. And then they deployed it and they

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