Surrogate optimization of variational quantum circuits

TL;DR

This paper introduces a surrogate optimization method for variational quantum circuits that uses classical simulators to improve convergence, demonstrated on a 40-qubit quantum processor.

Contribution

It presents a novel surrogate optimization approach combining classical simulation and Hessian approximation to enhance VQE convergence on near-term quantum hardware.

Findings

Efficient Hessian calculation using classical simulators.

Parallelizable optimization method for quantum circuits.

Successful demonstration on a 40-qubit quantum processor.

Abstract

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical CPU/GPU) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, passed as an input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.