Quantum generative modeling for financial time series with temporal correlations

ArXiv ID: 2507.22035 “View on arXiv”

Authors: David Dechant, Eliot Schwander, Lucas van Drooge, Charles Moussa, Diego Garlaschelli, Vedran Dunjko, Jordi Tura

Abstract

Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks. This is especially relevant for financial time series, since we only ever observe one realization of the process, namely the historical evolution of the market, which is further limited by data availability and the age of the market. However, for classical generative adversarial networks it has been shown that generated data may (often) not exhibit desired properties (also called stylized facts), such as matching a certain distribution or showing specific temporal correlations. Here, we investigate whether quantum correlations in quantum inspired models of QGANs can help in the generation of financial time series. We train QGANs, composed of a quantum generator and a classical discriminator, and investigate two approaches for simulating the quantum generator: a full simulation of the quantum circuits, and an approximate simulation using tensor network methods. We tested how the choice of hyperparameters, such as the circuit depth and bond dimensions, influenced the quality of the generated time series. The QGAN that we trained generate synthetic financial time series that not only match the target distribution but also exhibit the desired temporal correlations, with the quality of each property depending on the hyperparameters and simulation method.

Keywords: Quantum Generative Adversarial Networks (QGANs), Synthetic Data Generation, Tensor Network Methods, Financial Time Series, Temporal Correlations, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced quantum information concepts, variational quantum algorithms, and tensor network methods (MPS) requiring significant mathematical abstraction, while its validation relies primarily on qualitative comparison of simulated time series against stylized facts rather than on live backtesting or extensive statistical benchmarking against real market data.

  flowchart TD
    A["Research Goal: Can Quantum Correlations<br>Generate Financial Time Series with<br>Temporal Properties?"] --> B["Methodology: Quantum Generative<br>Adversarial Network"]
    B --> C{"Simulation Methods"}
    C --> D["Full Quantum Circuit Simulation"]
    C --> E["Tensor Network Approximation"]
    B --> F["Data Input: Historical Financial<br>Equity Time Series"]
    F --> G["Training Process: Generator creates<br>synthetic data; Discriminator classifies<br>real vs. synthetic. Hyperparameters<br>varied: Circuit Depth & Bond Dimension."]
    G --> H["Key Findings: QGANs successfully<br>generate data matching target<br>distribution and temporal correlations.<br>Quality depends on hyperparameters<br>and simulation method."]