Poster
Differentiable Analog Quantum Computing for Optimization and Control
Jiaqi Leng · Yuxiang Peng · Yi-Ling Qiao · Ming Lin · Xiaodi Wu
Hall J (level 1) #934
Keywords: [ auto-differentiation ] [ differentiable programming ] [ quantum control ] [ analog quantum computing ] [ Optimization ]
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}.