Publications

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, a leading publication in the field of quantitative finance. Summary The paper presents a fully quantum algorithm for pricing American options. The proposed method combines the Quantum Binomial Tree with Quantum Machine Learning to learn the optimal stopping rule directly on a quantum computer. Classical methods for pricing American options, such as Least-Squares Monte Carlo, require storing all simulated paths in memory to perform backward induction. For high-dimensional problems, this memory requirement becomes a limiting factor. The quantum approach presented in the paper avoids this bottleneck by representing the evolution of the stochastic process directly within the quantum state, offering an alternative computational framework for optimal stopping problems. Authors Rafał Pracht, finQbit Professor Dariusz Gatarek, finQbit Publication Wilmott Magazine, July 2026 issue.

In this paper, we study whether current quantum hardware can already be used for practical financial modeling. We propose a fully quantum approach to option pricing based on Quantum Neural Networks and implement it end-to-end on real quantum devices. The model is benchmarked within the Black–Scholes–Merton model framework, which allows us to evaluate accuracy in a controlled setting. The results show that even with today’s NISQ hardware, it is possible to approximate option pricing functions with a good level of precision. The broader goal is not to replace classical models, but to understand where quantum approaches can start to add value, especially for more complex models such as stochastic volatility or interest rate frameworks. Happy to discuss with anyone working at the intersection of quantum computing and quantitative finance.

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.

As time series feature unstructured data and unique processing requirements, they do not easily fit conventional approaches to quantum modeling. This paper therefore presents various quantum model architectures suitable for encoding and analysis of time series data. In particular, it investigates their architectural design aspects, such as methods of encoding and reuploading of temporal data, parameterizing quantum circuits, controlling the circuit qubit resources and entanglement, and dealing with issues of state evolution in the model Hilbert space, as well as navigability of the classical parameter space for an optimizer. Each approach enhances or impedes model expressivity, ie its ability to effectively represent time series data in quantum space, as well as its trainability, ie its capacity to learn and generalize for predictive accuracy and efficiency in the process of model optimization

This paper explains the main design decisions in the development of variational quantum time series models and denoising quantum time series autoencoders. Although we cover a specific type of quantum model, the problems and solutions are generally applicable to many other methods of time series analysis. The paper highlights the benefits and weaknesses of alternative approaches to designing a model, its data encoding and decoding, ansatz and its parameters, measurements and their interpretation, and quantum model optimization. Practical issues in training and execution of quantum time series models on simulators, including those that are CPU and GPU based, as well as their deployment on quantum machines, are also explored. All experimental results are evaluated, and the final recommendations are provided for the developers of quantum models focused on time series analysis.

This paper shows how information about the network’s community structure can be used to define node features with high predictive power for classification tasks. To do so, we define a family of community-aware node features and investigate their properties. Those features are designed to ensure that they can be efficiently computed even for large graphs. We show that community-aware node features contain information that cannot be completely recovered by classical node features or node embeddings (both classical and structural) and bring value in node classification tasks. This is verified for various classification tasks on synthetic and real-life networks.