Differentiable Analog Quantum Computing for Optimization and Control
This work addresses the challenge of efficiently exploiting near-term quantum devices for optimization and control, representing a novel method rather than an incremental improvement.
The authors tackled the problem of optimizing and controlling near-term quantum devices by introducing the first differentiable analog quantum computing framework, which achieved orders of magnitude improvement over state-of-the-art methods based on parameterized digital quantum circuits.
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.