Publications

Our work is gaining global recognition! We’re proud to share that our research paper “Quantum Binomial Tree: An Effective Method for Probability Distribution Loading for Derivative Pricing”, co-authored with the renowned Professor Dariusz Gatarek and our CTO Rafał Pracht, will appear in the September 2025 issue of Wilmott Magazine – one of the world’s most respected publications in quantitative finance. This research introduces the Quantum Binomial Tree framework, a novel and efficient method for probability distribution loading on quantum hardware, a key step for quantum Monte Carlo techniques. By enabling exponential scaling of simulated paths and delivering a quadratic speed-up versus classical methods, this approach brings us closer to accelerating the pricing of highly complex financial instruments. At finQbit, we’re proud to be at the edge of quantum finance innovation, contributing not only to real-world applications but also to the academic dialogue shaping the future of the industry. Being published in Wilmott is both an honor and recognition that our work is resonating with the global quant community.

This paper proposes a novel approach to pricing American options on quantum computers. Before the article, only one method for that purpose was proposed. The novel algorithm combines Quantum Binomial Tree and Quantum Machine Learning to implement the direct method for pricing American options on a quantum computer. It utilizes the quantum amplitude estimation algorithm with a quadratic speed-up over the classical Monte Carlo. This method allows us to exploit the exponential growth in the state vector on quantum computers and, by this, to overcome the limitations on memory on classical techniques.

The quantum algorithm for generating or loading a probability distribution on quantum computers is fundamental in Quantum Monte Carlo. While the quantum amplitude estimation algorithm gives a quadratic speed-up compared to classical Monte Carlo, the whole improvement may be lost in cases when the method of distribution loading is ineffective. On the other hand, the technique should be flexible enough to load any of the market models. Unfortunately, no such method till now was proposed. In the paper, I propose a new effective approach to the probability distribution loading for derivative pricing. The Quantum Binomial Tree is the quantum implementation of the classical Binomial Tree. This method allows us to increase the number of Monte Carlo paths exponentially on a quantum computer. I introduce how to load the local volatility model, the Heston model, the SABR model, and how to model any arbitrary multidimensional system of stochastic differential equations to quantum computers. I also demonstrate the detailed implementation with the source code of option pricing with time-dependent volatility. I also show the numerical results and conduct the complexity analysis.