Differentiable Analog Quantum Computing for Optimization and Control

Autor:	Leng, Jiaqi, Peng, Yuxiang, Qiao, Yi-Ling, Lin, Ming, Wu, Xiaodi
Rok vydání:	2022
Předmět:	Quantum Physics Computer Science - Machine Learning
Zdroj:	In the Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Druh dokumentu:	Working Paper
Popis:	We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by orders of magnitude. Comment: Code available at https://github.com/YilingQiao/diffquantum
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.15812 Zobrazit plný text záznamu View this record from Arxiv