aiqbench analysis
This research presents the first quantum algorithm to achieve an asymptotic speedup over classical combinatorial methods for maximum-weight perfect matching in general graphs, specifically improving performance in dense regimes. By integrating quantum techniques into the Duan-Pettie-Su framework and optimizing QRAM data structures, the study addresses a previously unsolved bottleneck in fundamental graph problems. This advancement marks a significant theoretical milestone for quantum computing applications in optimization and network analysis.
A Quantum Scaling Algorithm for Maximum-Weight Perfect Matching in General Graphs
arXiv:2609.39457v1 Announce Type: cross Abstract: Quantum speed-ups have been obtained for many fundamental graph problems, including most variants of matching. A notable exception, however, is the maximum-weight perfect matching (MWPM) problem in general graphs with integer edge weights, which is arguably the most challenging variant of matching. We present a quantum algorithm for MWPM in general graphs that runs in \( \widetilde{O}(n m^{2/3}\log W) \) time, where $W$ is an upper bound on the magnitude of the edge weights. This is an improvement over the best known classical combinatorial bound of \( \widetilde{O}(m\sqrt n \log W) \) in the dense regime, where $m\ge n^{3/2}$. To the best of our knowledge, this is the first quantum algorithm to obtain an asymptotic improvement over the best classical combinatorial algorithm for the MWPM problem in general graphs. The running time of our method accounts for QRAM initialization and access overheads up to polylogarithmic factors, as well as all classical updates to the data structures. At a high level, our algorithm is based on a classical framework due to Duan, Pettie, and Su, but our algorithm requires replacing certain classical tasks with quantum methods, alternative analysis of classical procedures, and the use of alternative data structures that can be implemented effectively in the QRAM model.
Abstract:Quantum speed-ups have been obtained for many fundamental graph problems, including most variants of matching. A notable exception, however, is the maximum-weight perfect matching (MWPM) problem in general graphs with integer edge weights, which is arguably the most challenging variant of matching. We present a quantum algorithm for MWPM in general graphs that runs in \( \widetilde{O}(n m^{2/3}\log W) \) time, where $W$ is an upper bound on the magnitude of the edge weights. This is an improvement over the best known classical combinatorial bound of \( \widetilde{O}(m\sqrt n \log W) \) in the dense regime, where $m\ge n^{3/2}$. To the best of our knowledge, this is the first quantum algorithm to obtain an asymptotic improvement over the best classical combinatorial algorithm for the MWPM problem in general graphs. The running time of our method accounts for QRAM initialization and access overheads up to polylogarithmic factors, as well as all classical updates to the data structures. At a high level, our algorithm is based on a classical framework due to Duan, Pettie, and Su, but our algorithm requires replacing certain classical tasks with quantum methods, alternative analysis of classical procedures, and the use of alternative data structures that can be implemented effectively in the QRAM model.
Data Structures and Algorithms (cs.DS); Quantum Physics (quant-ph)
arXiv:2609.39457 [cs.DS]
(or arXiv:2609.39457v1 [cs.DS] for this version)
https://doi.org/10.48550/arXiv.2609.39457 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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