Differentiable Analog Quantum Computing for Optimization and ControlDownload PDF

Published: 31 Oct 2022, Last Modified: 22 Oct 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: analog quantum computing, differentiable programming, auto-differentiation, optimization, quantum control
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.
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}.
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