Tearsheet: Goldman Sachs’ Will Zeng on financial quantum

On the Tearshet podcast, Goldman Sachs’ head of Quantum Research, Will Zeng, and Stefan Woerner, IBM’s lead for Quantum Applications Research & Software discussed recent findings they’ve made in quantum computing’s ability to address derivative pricing and more broadly, talked about the technology’s potential impact on financial services.

Will Zeng explained why he’s excited about quantum computing’s application in financial services and how it will change the rules of the game. There are big strides being made in bringing this technology to market: a financial ecosystem is already growing up around this massive shift in technology.

Will Zeng: One of the reasons that finance is so exciting for applying new kinds of commercial computing technologies is that we have a lot of mathematical problems that are pretty easy to specify and that there’s been a lot of incentive to work with classical computing hardware, non quantum. The old generations of hardware have been optimized aggressively in a lot of cases. So there’s a real need for new thinking.

There are three broad categories that we’re looking at right now. The first is in what I call broadly simulations. The paper that Stefan alluded to that we worked on was for the pricing of derivatives. The math setup is calculating expectation values of stochastic processes, functions, processes, which comes up all the time in finance around this modeling.

The second is optimization. There are a lot of hard optimization problems across financial services, portfolio optimization being maybe the most obvious one, but they come up in all sorts of places.

The third category is machine learning. And here, there are applications in trading, but there’s also applications in things like anti-money laundering, or avoiding fraud. And those three categories are already really, really large. And so what our research group is doing is trying to pick out more specific benchmarks where we think that quantum computing could be most useful first.

We still have just prototypes. Some of the optimization use cases that we’re excited about are ones where we have strong theoretical proof of big possible improvements. So, for example, in derivative pricing, at a theoretical level, you can show very generically that you can get tens, hundreds or thousands of decks sped up in certain kinds of theoretical models for how to do the problem. Then what we do is try to take that theory and apply it to machine learning.

Classical machine learning hasn’t totally penetrated finance yet. Regular ML is still spreading throughout financial services. And so that means that the benchmark is a little more vague. With the first few generations of quantum computers, we have less of a theoretical handle on how much better they might be. It’s a really, really active area of research. But it’s a bit more nascent.

It might be worth mentioning, when you think about where the field is going, the first prototype quantum computers have only been available in the last couple years. When Stefan and I started, it was just a thing that some academics and some deep research groups like IBM did. Now, there’s been more than $20 billion of global government public research funding announced over the last few years. Venture capital has invested almost $1.5 billion into the space. The growth and trajectory is really encouraging. We see a lot of innovation happening.

When I listen to stuff about the ecosystem, it’s like where machine learning was 10 or 12 years ago. Like where the compute power is not quite there yet. But as quantum hardware matures, and there’s a lot of roadmaps out there and a lot of money going into starting to build these machines, big ecosystems will pop up. New applications will continue to be found. We are, in some ways, here to champion that financial services is a really great place to start applying this tech. It’s got concrete problems, got global access to deploy new kinds of compute solutions. And so, part of this work is to sort of trumpet that it’s a good place for quantum computing to develop.

There’s maybe 150 companies in the ecosystem if you think of quantum tech a little broadly. There’s a whole stack of things here, like people who make hardware, there’s people who make control systems for the hardware, people who make software or applications research. And that’s really global. You see a lot of the big startups in the US but also in Europe, Australia, Japan, Netherlands, France, Germany, and the UK. It’s starting to emerge into real industry.

Listen to the podcast and read the full interviews


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