LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks

TL;DR

This paper introduces LAWS, a novel optimization algorithm for quantum neural networks that mitigates barren plateaus by reinitializing parameters based on gradient sampling, leading to improved convergence and generalization.

Contribution

LAWS is a new combinatorial optimization strategy that reinitializes parameters to overcome barren plateaus in variational quantum algorithms, with a unified framework and convergence guarantees.

Findings

LAWS effectively mitigates barren plateaus in quantum neural networks.

The proposed framework improves convergence speed and generalization in quantum classification.

Experimental results demonstrate superior performance over existing methods.

Abstract

Variational quantum algorithms (VQAs) have recently received significant attention from the research community due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized parameters are characterized by barren plateaus (BP) where the gradient vanishes exponentially in the number of qubits. In this paper, we first review quantum natural gradient (QNG), which is one of the most popular algorithms used in VQA, from the classical first-order optimization point of view. Then, we proposed a \underline{L}ook \underline{A}round \underline{W}arm-\underline{S}tart QNG (LAWS) algorithm to mitigate the widespread existing BP issues. LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG. LAWS repeatedly reinitializes parameter search space for the next iteration parameter update. The reinitialized parameter search space is carefully chosen by sampling the gradient close to the current optimal. Moreover, we present a unified framework (WS-SGD) for integrating parameter initialization techniques into the optimizer. We provide the convergence proof of the proposed framework for both convex and non-convex objective functions based on Polyak-Lojasiewicz (PL) condition. Our experiment results show that the proposed algorithm could mitigate the BP and have better generalization ability in quantum classification problems.