Estimating the gradient and higher-order derivatives on quantum hardware

For a large class of variational quantum circuits, we show how arbitrary-order derivatives can be analytically evaluated in terms of simple parameter-shift rules, i.e., by running the same circuit with different shifts of the parameters. As particular cases, we obtain parameter-shift rules for the Hessian of an expectation value and for the metric tensor of a variational state, both of which can be efficiently used to analytically implement second-order optimization algorithms on a quantum computer. We also consider the impact of statistical noise by studying the mean-square error of different derivative estimators. Some of the theoretical techniques for evaluating quantum derivatives are applied to their typical use case: the implementation of quantum optimizers. We find that the performance of different estimators and optimizers is intertwined with the values of different hyperparameters, such as the step size or the number of shots. Our findings are supported by several numerical and hardware experiments, including an experimental estimation of the Hessian of a simple variational circuit and an implementation of the Newton optimizer.

Automated summary

For a large class of variational quantum circuits, arbitrary-order derivatives can be analytically evaluated using simple parameter-shift rules, which can be efficiently used to implement second-order optimization algorithms on a quantum computer. The impact of statistical noise on derivative estimators is considered, with the performance of different estimators and optimizers found to be intertwined with the values of different hyperparameters. Numerical and hardware experiments support these findings, including an estimation of the Hessian of a variational circuit and an implementation of the Newton optimizer.