Quantum-accelerated conjugate gradient method via spectral initialization

TL;DR

This paper introduces a quantum-accelerated conjugate gradient method that uses quantum algorithms to generate initial guesses, improving efficiency in solving large-scale linear systems with potential advantages over classical methods.

Contribution

It proposes a hybrid quantum-classical approach that leverages quantum spectral initialization to enhance classical conjugate gradient solvers for large linear systems.

Findings

QACG can outperform classical methods in certain regimes.

It requires fewer quantum resources than full quantum linear solvers.

The approach offers a practical pathway for early fault-tolerant quantum computing applications.

Abstract

Solving large-scale linear systems problems is a cornerstone in scientific and industrial computing. Classical iterative solvers face increasing difficulty as the number of unknowns becomes large, while fully quantum linear solvers require fault-tolerant resources that remain far beyond near-term feasibility. Here we propose a quantum-accelerated conjugate gradient (QACG) method in which a fault-tolerant quantum algorithm is used exclusively to construct a spectrally informed initial guess for a classical conjugate gradient (CG) solver. We estimate the total runtime and resource requirements of an integrated quantum-HPC platform for the 3D Poisson equation. A central feature of QACG is the controllable decomposition of the condition number between the quantum and the classical solver, enabling flexible allocation of computational effort. Under explicit architectural assumptions, we identify regimes in which QACG yields a runtime advantage over purely classical approaches while requiring substantially fewer quantum resources than end-to-end quantum linear solvers. These results illustrate a concrete pathway toward the scientific and industrial use of early-stage fault-tolerant quantum computing and point to a integrated paradigm in which quantum devices act as accelerators within high-performance computing workflows.