Differentiable Analog Quantum Computing for Optimization and Control

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

Hall J #934

Keywords: [ Optimization ] [ analog quantum computing ] [ quantum control ] [ differentiable programming ] [ auto-differentiation ]

[ Abstract ]
[ Paper [ OpenReview
Thu 1 Dec 9 a.m. PST — 11 a.m. PST
Spotlight presentation: Lightning Talks 2B-2
Tue 6 Dec 5:30 p.m. PST — 5:45 p.m. PST


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}.

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