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.

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.

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.

A community structure that is often present in complex networks plays an important role not only in their formation but also shapes dynamics of these networks, affecting properties of their nodes. In this paper, we propose a family of community-aware node features and then investigate their properties. We show that they have high predictive power for classification tasks. We also verify that they contain information that cannot be recovered completely neither by classical node features nor by classical or structural node embeddings.