Reasoning about RoboCup soccer narratives
Hannaneh Hajishirzi, Julia Hockenmaier, et al.
UAI 2011
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.
Hannaneh Hajishirzi, Julia Hockenmaier, et al.
UAI 2011
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Rie Kubota Ando
CoNLL 2006
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM