Can Classical Initialization Help Variational Quantum Circuits Escape the Barren Plateau?

TL;DR

This paper investigates whether classical deep learning initialization strategies can help mitigate the barren plateau problem in variational quantum algorithms, finding limited overall benefits through extensive experiments.

Contribution

It systematically adapts classical initialization methods to VQAs, evaluates their effectiveness, and provides a foundation for future research in this area.

Findings

Classical initialization heuristics offer moderate improvements in some cases.

Overall benefits of classical initializations are marginal.

Provides a framework and future directions for further exploration.

Abstract

Variational quantum algorithms (VQAs) have emerged as a leading paradigm in near-term quantum computing, yet their performance can be hindered by the so-called barren plateau problem, where gradients vanish exponentially with system size or circuit depth. While most existing VQA research employs simple Gaussian or zero-initialization schemes, classical deep learning has long benefited from sophisticated weight initialization strategies such as Xavier, He, and orthogonal initialization to improve gradient flow and expedite convergence. In this work, we systematically investigate whether these classical methods can mitigate barren plateaus in quantum circuits. We first review each initialization's theoretical grounding and outline how to adapt the notions from neural networks to VQAs. We then conduct extensive numerical experiments on various circuit architectures and optimization tasks. Our findings indicate that while the initial heuristics, inspired by classical initialization, yield moderate improvements in certain experiments, their overall benefits remain marginal. By outlining a preliminary exploration plan in this paper, we aim to offer the research community a broader perspective and accessible demonstrations. Furthermore, we propose future research directions that may be further refined by leveraging the insights gained from this work.