Conditional Generative Models for Learning Stochastic Processes

Salvatore Certo, Anh Pham, Nicolas Robles and Andrew Vlasic

Papers from arXiv.org

Abstract: A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a more efficient state preparation procedure than current methods. This methodology has the potential to speed-up algorithms, such as Monte Carlo analysis. In particular, after demonstrating the effectiveness of the network in the learning task, the technique is applied to price Asian option derivatives, providing the foundation for further research on other path-dependent options.

Date: 2023-04, Revised 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm, nep-des and nep-sea
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Published in Quantum Machine Intelligence 2023

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