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Synthetic data generation with hybrid quantum-classical models for the financial sector

Author

Listed:
  • Otto M. Pires

    (SENAI-CIMATEC)

  • Mauro Q. Nooblath

    (SENAI-CIMATEC)

  • Yan Alef C. Silva

    (SENAI-CIMATEC)

  • Maria Heloísa F. Silva

    (SENAI-CIMATEC
    Universidade Federal do Oeste da Bahia - Campus Reitor Edgard Santos, UFOB)

  • Lucas Q. Galvão

    (SENAI-CIMATEC)

  • Anton S. Albino

    (SENAI-CIMATEC)

Abstract

Data integrity and privacy are critical concerns in the financial sector. Traditional methods of data collection face challenges due to privacy regulations and time-consuming anonymization processes. In collaboration with Banco BV, we trained a hybrid quantum-classical generative adversarial network (HQGAN), where a quantum circuit serves as the generator and a classical neural network acts as the discriminator, to generate synthetic financial data efficiently and securely. We compared our proposed HQGAN model with a fully classical GAN by evaluating loss convergence and the MSE distance between the synthetic and real data. Although initially promising, our evaluation revealed that HQGAN failed to achieve the necessary accuracy to understand the intricate patterns in financial data. This outcome underscores the current limitations of quantum-inspired methods in handling the complexities of financial datasets. Graphical abstract

Suggested Citation

  • Otto M. Pires & Mauro Q. Nooblath & Yan Alef C. Silva & Maria Heloísa F. Silva & Lucas Q. Galvão & Anton S. Albino, 2024. "Synthetic data generation with hybrid quantum-classical models for the financial sector," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(11), pages 1-11, November.
    • Handle: RePEc:spr:eurphb:v:97:y:2024:i:11:d:10.1140_epjb_s10051-024-00786-1
    DOI: 10.1140/epjb/s10051-024-00786-1
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    References listed on IDEAS

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    1. Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
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    4. Dmitry Efimov & Di Xu & Luyang Kong & Alexey Nefedov & Archana Anandakrishnan, 2020. "Using generative adversarial networks to synthesize artificial financial datasets," Papers 2002.02271, arXiv.org.
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