That’s a different use case where we were dealing with quantum-inspired methods. Option pricing is the problem of pricing complicated financial objects. There are some complicated financial objects called derivatives, which are complicated contracts depending on whether to buy or sell something in a given period of time. It’s a complicated financial thing, but people use them for investing. There is a huge problem in finance, which is how to give a price to these contracts. There are even Nobel Prizes in economics for this.
The way in which people nowadays are solving these problems is, with classical computers, using huge Monte Carlo simulations. They are dealing with lots of problems of distribution, stochastic variables and so on. There is now a trend to use deep learning, techniques based on artificial intelligence where you have a particular type of structure that is learning how to perform a particular task. When it comes to option pricing, there are different deep-learning algorithms where you can use neural networks to solve the problem to a neutral price.
Now, it turns out that came to us and asked us, “What do you think about this problem? Do you think we can solve it?” We started discussing with them. At the end, we did a project with them on derivative pricing in finance using quantum-inspired methods.
Now, internally at the bank, they had a deep-learning strategy for solving this that was very efficient. We improved it by using tensor networks, which are quantum-inspired. We developed a new type of classical algorithm inspired by quantum that was mixing neural networks with tensor networks. We were able to assess the inefficiencies of neural networks and improve them using tensor networks. This turned out to be a huge improvement. We could improve the time in the calculations by a large factor. We could improve the precision, the ability of the calculation and so on to the point that the guys from were so happy with this that they just decided that they would implement this into production. This worked out very well.
This is an example of a different type of quantum-inspired algorithm that is not optimisation. It turns out that this is an machine learning algorithm with artificial intelligence. You know, there is all this mess about neural networks, deep learning, and generative AI and all of this. If you heard about ChatGPT nowadays is.
All these algorithms, they can be improved in precision and performance with quantum-inspired. This is my way of seeing this: Artificial intelligence, or at least neural networks, deep learning and so on, these are algorithms that were invented in the ’40s. People were not running these algorithms in the ’40s, because they didn’t have the machines that you need to run them. We have the machines now in 2020, 80 years later, but the algorithms are from the ’40s. So, in 80 years, we have come up with lots of developments that we should be implementing, and that’s exactly what we are doing now.