Classical Regularization in Variational Quantum Eigensolvers

TL;DR

This paper demonstrates that classical L2 regularization can stabilize variational quantum eigensolvers, improving their performance and robustness without changing the quantum circuit, across various Hamiltonians.

Contribution

It introduces a simple classical regularization technique to enhance the stability and reliability of VQEs, addressing optimization challenges in hybrid quantum-classical algorithms.

Findings

Classical L2 regularization improves VQE performance.

Regularization enhances stability across different Hamiltonians.

Method is system-independent and does not alter quantum circuits.

Abstract

While quantum computers are a very promising tool for the far future, in their current state of the art they remain limited both in size and quality. This has given rise to hybrid quantum-classical algorithms, where the quantum device performs only a small but vital part of the overall computation. Among these, variational quantum algorithms (VQAs), which combine a classical optimization procedure with quantum evaluation of a cost function, have emerged as particularly promising. However, barren plateaus and ill-conditioned optimization landscapes remain among the primary obstacles faced by VQAs, often leading to unstable convergence and high sensitivity to initialization. Motivated by this challenge, we investigate whether a purely classical remedy, standard L2 squared-norm regularization, can systematically stabilize hybrid quantum-classical optimization. Specifically, we augment the Variational Quantum Eigensolver (VQE) objective with a quadratic penalty proportional to the squared norm of the parameters, without modifying the quantum circuit or measurement process. Across all tested Hamiltonians, H2, LiH, and the Random Field Ising Model (RFIM), we observe improved performance over a broad window of the regularization strength. Our large-scale numerical results demonstrate that classical regularization provides a robust, system-independent mechanism for mitigating VQE instability, enhancing the reliability and reproducibility of variational quantum optimization without altering the underlying quantum circuit.