https://doi.org/10.1140/epjb/s10051-024-00786-1
Regular Article - Computational Methods
Synthetic data generation with hybrid quantum-classical models for the financial sector
1
Latin America Quantum Computing Center, SENAI-CIMATEC, Av. Orlando Gomes, 1845, 41650-010, Salvador, Bahia, Brazil
2
Universidade Federal do Oeste da Bahia - Campus Reitor Edgard Santos, UFOB, Rua Bertioga, 892, Morada Nobre I, 47810-059, Barreiras, Bahia, Brazil
Received:
26
February
2024
Accepted:
10
September
2024
Published online:
18
November
2024
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.
© The Author(s) 2024
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