A Residual-Based Quantum Linear System Algorithm with Dynamic Stopping and Applications to Elliptic PDEs
Xiantao Li

TL;DR
This paper introduces a residual-based quantum linear system algorithm with dynamic stopping, enabling on-the-fly convergence detection for elliptic PDE discretizations, reducing runtime and hardware errors.
Contribution
It develops a novel residual measurement method for quantum algorithms solving elliptic PDEs, allowing adaptive stopping without solution reconstruction.
Findings
Residual probability tracks actual error decay in experiments.
Dynamic stopping reduces quantum circuit runtime.
Method improves efficiency over fixed schedules for some problems.
Abstract
Quantum linear-system algorithms (QLSAs) have rigorous worst-case complexity guarantees, but their runtimes are often chosen from spectral information assumed in advance. What is largely lacking is an a posteriori progress flag: most QLSA workflows, unlike the classical counterparts, do not provide a built-in mechanism to signal whether a particular instance has already converged. For discretizations of elliptic PDEs with divergence--gradient structure \[ -\nabla\cdot \big(a(x)\nabla) \approx A_h=G_h^\dagger G_h, \] we formulate a stable first-order ODE whose limiting solution block is the desired Galerkin solution. The PDE-dependent scale is then \(\norm{G_h}=\bigO(h^{-1})\), comparable to factorized QLSA constructions with square-root condition-number scaling. We design an augmented dynamics with residual variables, in which measuring a…
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