Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization

TL;DR

This paper introduces a gate freezing technique for gradient-free optimization in variational quantum algorithms, enhancing convergence and robustness on noisy quantum hardware.

Contribution

It proposes novel gate freezing methods that utilize previous iteration information to improve gradient-free optimizer performance in PQC circuit optimization.

Findings

Improved convergence of gradient-free optimizers on NISQ devices.

Enhanced robustness of PQC optimization against noise and barren plateaus.

Consistent performance gains demonstrated across various optimization tasks.

Abstract

Parameterized quantum circuits (PQCs) are pivotal components of variational quantum algorithms (VQAs), which represent a promising pathway to quantum advantage in noisy intermediate-scale quantum (NISQ) devices. PQCs enable flexible encoding of quantum information through tunable quantum gates and have been successfully applied across domains such as quantum chemistry, combinatorial optimization, and quantum machine learning. Despite their potential, PQC performance on NISQ hardware is hindered by noise, decoherence, and the presence of barren plateaus, which can impede gradient-based optimization. To address these limitations, we propose novel methods for improving gradient-free optimizers Rotosolve, Fraxis, and FQS, incorporating information from previous parameter iterations. Our approach conserves computational resources by reallocating optimization efforts toward poorly optimized gates, leading to improved convergence. The experimental results demonstrate that our techniques consistently improve the performance of various optimizers, contributing to more robust and efficient PQC optimization.