Quantum Error Mitigation Strategies for Variational PDE-Constrained Circuits on Noisy Hardware

TL;DR

This paper systematically evaluates noise mitigation strategies for variational quantum circuits solving PDEs on noisy hardware, demonstrating the effectiveness of zero-noise extrapolation and the inherent noise resilience of physics-constrained circuits.

Contribution

It provides a comprehensive analysis of error mitigation techniques and reveals that physics constraints improve noise resilience in variational PDE solvers on NISQ devices.

Findings

Zero-noise extrapolation reduces error by up to 96%.

Physics-constrained circuits maintain higher fidelity under noise.

Probabilistic error cancellation is effective but has high sampling costs.

Abstract

Variational quantum circuits (VQCs) solving partial differential equations (PDEs) on near-term quantum hardware face a critical challenge: hardware noise degrades solution fidelity and disrupts convergence. We present a systematic study of three noise channels; depolarizing, amplitude damping, and bit-flip on VQCs constrained by PDE residual loss functions for the heat equation, Burgers' equation, and the Saint-Venant shallow water equations. We benchmark three error mitigation strategies: zero-noise extrapolation (ZNE) via Richardson polynomial fitting, probabilistic error cancellation (PEC), and measurement error mitigation through inverse confusion matrices. Our numerical experiments on 6-qubit, 4-layer circuits demonstrate that ZNE reduces absolute error by 82-96% at low noise (p = 0.001), with effectiveness degrading gracefully at higher noise strengths. We prove analytically and confirm numerically that physics-constrained circuits exhibit inherent noise resilience: at p = 0.01, constrained circuits maintain 25-47% higher fidelity than unconstrained counterparts, with the advantage scaling with PDE complexity. PEC provides near-exact correction at low gate counts but incurs exponential sampling overhead, rendering it impractical beyond ~60 gates at p >= 0.02. Error budget decomposition reveals that systematic errors dominate at all noise levels (43-58%), while the PDE residual component grows from ~10% to ~31% as noise increases, indicating that physics constraints absorb noise through structured gradient information. These results establish practical guidelines for deploying variational PDE solvers on NISQ hardware.