RT Journal Article
T1 A quantum interior-point predictor-corrector algorithm for linear programming
A1 Casares, P. A. M.
A1 Martín-Delgado Alcántara, Miguel Ángel
AB We introduce a new quantum optimization algorithm for dense linear programming problems, which can be seen as the quantization of the interior point predictor-corrector algorithm [1] using a quantum linear system algorithm [2]. The (worst case) work complexity of our method is, up to polylogarithmic factors, O(L root n(n+m)(parallel to M parallel to) over bar (F) (kappa)over bar> over bar epsilon(-2)) for n the number of variables in the cost function,mthe number of constraints,epsilon(-1)the target precision,Lthe bit length of the input data, (parallel to M parallel to) over bar (F) over bar <ian upper bound to the Frobenius norm of the linear systems of equations that appear, <(parallel to M parallel to)over bar>(F), and (kappa) over bar an upper bound to the condition number kappa of those systems of equations. This represents a quantum speed-up in the number n of variables in the cost function with respect to the comparable classical interior point algorithms when the initial matrix of the problem A is dense: if we substitute the quantum part of the algorithm by classical algorithms such as conjugate gradient descent, that would mean the whole algorithm has complexity O(L root n(n + m)(2) (kappa) over bar log(epsilon(-1))), or with exact methods, at least O(L root n(n + m)(2.373)). Also, in contrast with any quantum linear system algorithm, the algorithm described in this article outputs a classical description of the solution vector, and the value of the optimal solution.
PB IOP Publishing Ltd
SN 1751-8113
YR 2020
FD 2020-11-06
LK https://hdl.handle.net/20.500.14352/6703
UL https://hdl.handle.net/20.500.14352/6703
LA eng
NO © 2020 IOP Publishing Ltd.We acknowledge financial support from the Spanish MINECO Grants FIS2015-67411P, and the CAM research consortium QUITEMAD-CM, Grant No. S2018/TCS-4342. The research of MAM-D has been partially supported by the U.S. Army Research Office through Grant No. W911NF-14-1-0103. PAMC thanks the support of a FPU MECD Grant.
NO Ministerio de Economía y Competitividad (MINECO)
NO Comunidad de Madrid
DS Docta Complutense
RD 11 abr 2025