Differentiable Analog Quantum Computing for Optimization and Control

• URL: http://arxiv.org/abs/2210.15812v1
• Date: Fri, 28 Oct 2022 00:28:31 GMT
• Title: Differentiable Analog Quantum Computing for Optimization and Control
• Authors: Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao, Ming Lin, Xiaodi Wu
• Abstract summary: We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level. We propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling.
• Score: 14.736412617211538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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