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CQC develops algo to accelerate Monte Carlo integration with quantum computers

CQC develops algo to accelerate Monte Carlo integration with quantum computers

Cambridge Quantum Computing (CQC) says it has discovered an algorithm that accelerates quantum Monte Carlo integration.

Monte Carlo integration - the process of numerically estimating the mean of a probability distribution by averaging samples - is used to evaluate risk and simulate prices for a variety of financial instruments.

Using traditional hardware, the complex calculations needed for Monte Carlo are typically executed once overnight, which means that in volatile markets, traders are forced to use outdated results.

CQG says it has "solved" this problem with an algorithm detailed in a released pre-print of a paper by Senior Research Scientist, Steven Herbert, "showing how historic challenges are eliminated, and the full quadratic quantum advantage is obtained".

Says Herbert: "This new algorithm is a historic advance which expands quantum Monte Carlo integration and will have applications both during and beyond the NISQ era. We are now capable of achieving what was previously only a theoretical quantum speed-up."

Last month, Goldman Sachs claimed that it had made a breakthrough that could see Monte Carlo simulations sped up using near-term quantum hardware expected to be available in just five to 10 years. To do this, researchers from the bank and partner QC Ware sacrificed some of the speed up from 1000x to 100x to produce shallow Monte Carlo algorithms.

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