The gross market value of outstanding OTC derivatives (options and futures) was $13 trillion in 2021, while over 63 billion exchange-traded derivatives contracts were traded.
These huge volumes mean that even a minor accuracy improvement in the pricing of complex derivatives can have a very significant financial implication.
Derivative pricing is typically done through Monte Carlo simulation, which can require significant compute resources and runtime.
An alternative is to apply machine learning, either by learning from past data, suffering from generalization inaccuracies or by solving SDE-based models describing the time evolution of e.g. prices, which can be hard to link to real-world observations.
PASQAL has developed unique and proprietary (quantum) neural network methods, which compute statistical properties like expected price or variance from a combination of some historic market data and an SDE-based market model.
Our methods efficiently solve integro-differential equations and run either on large GPU clusters or on our neutral atoms quantum computers.
Quantum in Real Life
Crédit Agricole CIB
"Quantum computing is radically different from almost everything we know and use today, in terms of theory, hardware and algorithms. This project will assemble many different competencies around the table: bankers, physicists, mathematicians, computer scientists, IT architects, all cooperating to this remarkable journey. This is a huge challenge, and we are confident to make it a success, jointly with our talented partners PASQAL and Multiverse Computing.”
Ali El Hamidi
Department Head Capital Markets Funding - Global Markets Division