Conditional Generative Models for Learning Stochastic Processes. (arXiv:2304.10382v2 [quant-ph] UPDATED)

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, …

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