Kernel Approximation Based Optimization for Variational Quantum Algorithms

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we develop a technique for variational parameter optimization by reconstructing local patches of a cost function based on batches of noisy quantum circuit results. We compare this technique to commonly-used optimization techniques for noisy data. We also discuss its convergence properties and applicability to NISQ devices.