Quantum computing is now being used for a number of financial applications, including fraud detection, stock price prediction, portfolio optimization, and product recommendation.
The Schrödinger-based equation that Haven derives has a parameter ħ (not to be confused with the complex conjugate of h) that represents the amount of arbitrage that is present in the market resulting from a variety of sources including non-infinitely fast price changes, non-infinitely fast information dissemination and unequal wealth among traders.
[9][10] Andrei Khrennikov builds on the work of Haven and others and further bolsters the idea that the market efficiency assumption made by the Black–Scholes–Merton equation may not be appropriate.
[11] To support this idea, Khrennikov builds on a framework of contextual probabilities using agents as a way of overcoming criticism of applying quantum theory to finance.
Luigi Accardi and Andreas Boukas again quantize the Black–Scholes–Merton equation, but in this case, they also consider the underlying stock to have both Brownian and Poisson processes.
Patrick Rebentrost showed in 2018 that an algorithm exists for quantum computers capable of pricing financial derivatives with a square root advantage over classical methods.