PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems

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 $2.3\times10^{-8}$ p.u.\ in voltage magnitude and $3.4\times10^{-6}$ degrees in voltage angle.