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We formulate the first differentiable analog quantum computing framework witha specific parameterization design at the analog signal (pulse) level to betterexploit near-term quantum devices via variational methods. We further propose ascalable approach to estimate the gradients of quantum dynamics using a forwardpass with Monte Carlo sampling, which leads to a quantum stochastic gradientdescent algorithm for scalable gradient-based training in our framework.Applying our framework to quantum optimization and control, we observe asignificant advantage of differentiable analog quantum computing against SOTAsbased on parameterized digital quantum circuits by orders of magnitude.

Differentiable Analog Quantum Computing for Optimization and Control