finQbit next Paper in Wilmott Magazine

finQbit’s paper, “The Pricing of American Options on the Quantum Computer,” co-authored by Rafał Pracht and Professor Dariusz Gatarek, has been published in the July 2026 issue of Wilmott Magazine, one of the leading publications in quantitative finance.

The Problem: Pricing American Options

Unlike European options, American options can be exercised at any point before expiration. This means their pricing requires solving an optimal stopping problem, determining at which point the option holder should exercise it in order to maximise the payoff.

Classical methods used for this task, such as Least-Squares Monte Carlo, rely on backward induction and require storing all simulated paths of the stochastic process in memory. For high-dimensional problems, those involving multiple correlated risk factors, this memory requirement, rather than raw computational power, becomes the primary limitation.

The Proposed Approach

The authors present what is, to the best of their knowledge, the first fully quantum algorithm for pricing American options. The method combines two elements:

Quantum Binomial Tree a quantum counterpart of the binomial tree, used to model the evolution of the underlying asset price,
Quantum Machine Learning used to learn the optimal stopping rule directly on a quantum computer.

The key difference from classical approaches is that the evolution of the stochastic process is represented directly within the quantum state, rather than stored as a set of explicitly recorded paths. This allows the method to avoid the memory bottleneck that grows with the dimensionality of the problem in classical approaches.

Significance for the Field

Discussions about the applications of quantum computing in finance typically focus on computational speed. The paper published in Wilmott Magazine points to a different aspect, the ability to bypass structural memory limitations that constrain classical methods when pricing instruments dependent on multiple risk factors.

The findings are part of finQbit’s broader research direction, focused on applying quantum and classical computing to financial risk management infrastructure.