Paper ID: 2210.15812

Differentiable Analog Quantum Computing for Optimization and Control

Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao, Ming Lin, Xiaodi Wu

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.

Submitted: Oct 28, 2022