analiza aiqbench
PyQLAS is a software library that splits linear-system solves between a quantum subroutine and a classical host, with the split exposed as a tunable parameter. Its main evidence is a 118-bus power-flow test in which the solver matches a classical reference to very small error, and it runs unchanged on simulators and a real superconducting processor. Readers should note that this is a library and validation study, not a demonstration of quantum speedup, and the problem sizes tested are small.
PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems
arXiv:2610.41317v1 Announce Type: new 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 $2.3\times10^{-8}$ p.u.\ in voltage magnitude and $3.4\times10^{-6}$ degrees in voltage angle.
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 $2.3\times10^{-8}$ p.u.\ in voltage magnitude and $3.4\times10^{-6}$ degrees in voltage angle.
arXiv:2610.41317 [quant-ph]
(or arXiv:2610.41317v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.41317 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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