PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems
Abstract
Quantum algorithms solve a linear system at a cost that grows only polylogarithmically in its dimension, but on present-day hardware that advantage is realised only if the quantum and the classical parts of a solve are apportioned deliberately. We present PyQLAS, a quantum--classical hybrid library for linear systems that exposes this apportionment as a design parameter rather than a fixed assumption. PyQLAS builds three nested solvers around a single variational core---a variational solver, an outer residual iteration, and a Krylov-subspace projection---so that the quantum subroutine can be held at a fixed circuit width while the classical host absorbs the growth of the problem. Around them, the library supplies preconditioning at two independent levels, dimension alignment, checkpointing, and device-budget control, and a solve written against the interface runs unchanged on an exact state-vector simulator, on a local sampler, and on a real superconducting processor. The library is validated on the IEEE 118-bus power flow, a standard test system in which the linear solver is embedded in a classical Newton iteration. There, the iteration converges to the specified tolerance and reproduces the classical reference solution to within 2.3×10−8 p.u.\ in voltage magnitude and 3.4×10−6 degrees in voltage angle.
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BibTeX
@misc{dou2026pyqlas,
title = {{PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems}},
author = {Meng-Han Dou and Teng-Yang Ma and Chuang-Chao Ye and Zhao-Yun Chen and Guo-Ping Guo},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41317},
eprint = {2610.41317},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41317},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems
AU - Meng-Han Dou
AU - Teng-Yang Ma
AU - Chuang-Chao Ye
AU - Zhao-Yun Chen
AU - Guo-Ping Guo
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41317
UR - https://arxiv.org/abs/2610.41317
AB - Quantum algorithms solve a linear system at a cost that grows only polylogarithmically in its dimension, but on present-day hardware that advantage is realised only if the quantum and the classical parts of a solve are apportioned deliberately. We present PyQLAS, a quantum--classical hybrid library for linear systems that exposes this apportionment as a design parameter rather than a fixed assumption. PyQLAS builds three nested solvers around a single variational core---a variational solver, an outer residual iteration, and a Krylov-subspace projection---so that the quantum subroutine can be held at a fixed circuit width while the classical host absorbs the growth of the problem. Around them, the library supplies preconditioning at two independent levels, dimension alignment, checkpointing, and device-budget control, and a solve written against the interface runs unchanged on an exact state-vector simulator, on a local sampler, and on a real superconducting processor. The library is validated on the IEEE 118-bus power flow, a standard test system in which the linear solver is embedded in a classical Newton iteration. There, the iteration converges to the specified tolerance and reproduces the classical reference solution to within 2.3×10^−8 p.u.\ in voltage magnitude and 3.4×10^−6 degrees in voltage angle.
ER -