An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

TL;DR

This paper introduces iCANS, an adaptive optimizer that efficiently manages measurement resources in variational quantum algorithms, outperforming existing optimizers especially under noisy conditions.

Contribution

The paper presents iCANS, a novel adaptive optimizer that dynamically adjusts measurement counts in VHQCAs, improving efficiency and performance over traditional methods.

Findings

iCANS outperforms state-of-the-art optimizers in simulations.

iCANS is particularly effective in noisy quantum environments.

Adaptive measurement strategies enhance VHQCA performance.

Abstract

Variational hybrid quantum-classical algorithms (VHQCAs) have the potential to be useful in the era of near-term quantum computing. However, recently there has been concern regarding the number of measurements needed for convergence of VHQCAs. Here, we address this concern by investigating the classical optimizer in VHQCAs. We introduce a novel optimizer called individual Coupled Adaptive Number of Shots (iCANS). This adaptive optimizer frugally selects the number of measurements (i.e., number of shots) both for a given iteration and for a given partial derivative in a stochastic gradient descent. We numerically simulate the performance of iCANS for the variational quantum eigensolver and for variational quantum compiling, with and without noise. In all cases, and especially in the noisy case, iCANS tends to out-perform state-of-the-art optimizers for VHQCAs. We therefore believe this adaptive optimizer will be useful for realistic VHQCA implementations, where the number of measurements is limited.