Bleximo Researchers Offer Demonstration to Better Understand Quantum in Finance

Bleximo researchers wrote a demonstration that outlines the steps needed to implement the quantum algorithm for Monte Carlo methods. (Image: Envato)

At the height of the pandemic in 2019, traders used a derivative — named a call option — to boost a relatively unappreciated and borderline tanking GameStop (GME) stock into one of the most highly valued company on NASDAQ. One trader used these options to turn a $50,000 investment into a $50 million position in a year.

The anecdote shows the power of derivatives. It doesn’t necessarily spotlight the risks, which are considerable, too. In fact, the infamous GameStop trade hints at an important use case for quantum computing to answer questions like: what is the price of these contracts and what is the best way to determine those prices?

A team of scientists from quantum computer startup Bleximo, which is focusing on building application-specific quantum hardware and appropriate software layers, have written a demonstration that outlines the steps needed to implement the quantum algorithm for Monte Carlo methods. The demonstration shows how to apply the quantum version of Monte Carlo to derivative pricing, covering the hardware requirements needed for running the algorithm.

Simulated asset price and Time StepAccording to the team, who posted about the demonstration on the company’s Medium blog, the call option is a contract that gives the owner the right to buy an asset at a fixed price — or strike price — before a set date, called the expiration date.

The scientists point out that in 1973, Fisher Black, Myron Scholes, and Robert Merton created a method to price derivatives and became the bedrock for financial analysis of derivatives, which are used in trillion-dollar risk management techniques by numerous institutions. (The gross market value of all over-the-counter (OTC) derivatives in the second half of 2021 was about 12 trillion USD.)

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Quantum computers could turn into tools to quickly and efficiently tackle Monte Carlo problems. In 2015, Ashley Montanaro published a quantum algorithm to accelerate Monte Carlo methods. However, quantum computers would need to be more mature than the current quantum devices. In other words, they would need a large number of qubits and low error rates to sufficiently accelerate Monte Carlo methods.

You can download the notebook here.

If you found this article to be informative, you can explore more current quantum news here, exclusives, interviews, and podcasts.

Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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