Differentiable Analog Quantum Computing for Optimization and Control

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

Keywords: analog quantum computing, differentiable programming, auto-differentiation, optimization, quantum control

Abstract: We formulate the first differentiable analog quantum computing framework with 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 {\em orders of magnitude}.

TL;DR: a scalable differentiable programming framework for quantum computing at the pulse (analog) level that demonstrates orders of magnitude advantages over SOTAs based on parameterized quantum circuits in quantum optimization and control.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.15812/code)