Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
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 the 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.
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Amarachi Blessing Mbakwe, Joy Wu, et al.
NeurIPS 2023
Paul G. Comba
Journal of the ACM
Seung Gu Kang, Jeff Weber, et al.
ACS Fall 2023