Reasoning about Noisy Sensors in the Situation Calculus
Fahiem Bacchus, Joseph Y. Halpern, et al.
IJCAI 1995
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.
Fahiem Bacchus, Joseph Y. Halpern, et al.
IJCAI 1995
Michael Muller, Anna Kantosalo, et al.
CHI 2024
Leonid Karlinsky, Joseph Shtok, et al.
CVPR 2019
Ran Iwamoto, Kyoko Ohara
ICLC 2023