Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits
This addresses a critical obstacle for practical use of near-term quantum devices by reducing measurement costs, though it appears incremental as it builds on existing SGD and BO methods.
The paper tackles the problem of excessive quantum-measurement shots needed for optimizing parameterized quantum circuits by developing SGLBO, which combines stochastic gradient descent with Bayesian optimization and noise-reduction techniques. Numerical simulations show the method drastically reduces measurement-shot cost and improves accuracy while making optimization noise-robust.
Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observables and repeating many iterations, whose cost has been a critical obstacle for practical use. We develop an efficient alternative optimization algorithm, stochastic gradient line Bayesian optimization (SGLBO), to address this problem. SGLBO reduces the measurement-shot cost by estimating an appropriate direction of updating circuit parameters based on stochastic gradient descent (SGD) and further utilizing Bayesian optimization (BO) to estimate the optimal step size for each iteration in SGD. In addition, we formulate an adaptive measurement-shot strategy and introduce a technique of suffix averaging to reduce the effect of statistical and hardware noise. Our numerical simulation demonstrates that the SGLBO augmented with these techniques can drastically reduce the measurement-shot cost, improve the accuracy, and make the optimization noise-robust.