Differentiable Analog Quantum Computing for Optimization and Control. (arXiv:2210.15812v1 [quant-ph])

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 …

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