Goldman Sachs, IBM Researchers Use New Approach to Estimate Quantum Computing Resources Needed for Advantage in Derivative Pricing

(IBMQuantumBlog) Quantum research teams at IBM and Goldman Sachs provide the first detailed estimate of the quantum computing resources needed to achieve quantum advantage for derivative pricing — one of the most ubiquitous calculations in finance.
In their paper on arXiv, “A Threshold for Quantum Advantage in Derivative Pricing”, they describe the challenges in previous quantum approaches to derivative pricing, and introduce a new method for overcoming those obstacles. The new approach — called the re-parameterization method — combines pre-trained quantum algorithms with approaches from fault-tolerant quantum computing to dramatically reduce the estimated resource requirements for pricing financial derivatives using quantum computers.
The reseasrchers describe the challenges in previous quantum approaches to derivative pricing, and we introduce a new method for overcoming those obstacles. The new approach — called the re-parameterization method — combines pre-trained quantum algorithms with approaches from fault-tolerant quantum computing to dramatically reduce the estimated resource requirements for pricing financial derivatives using quantum computers.
The researchers focused on derivative pricing, but our work could also apply to other kinds of risk calculations. Derivatives are a good place to start because enormous sums of derivatives are traded each year, globally. A derivative contract is a financial asset whose estimated value is based on how the price of some underlying asset(s) — such as futures, options, stocks, currencies and commodities — change over time. The ability to more accurately price or assess the risk inherent in each of those contracts — even if the advantage is relatively small — could have a large impact on the financial services industry.