Optimal Scheduling of Graph States via Path Decompositions

Fuente: arXiv
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Bibliographic Details
Main Authors: Elman, Samuel J., Gavriel, Jason, Mann, Ryan L.
Format: Preprint
Published: 2024
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author Elman, Samuel J.
Gavriel, Jason
Mann, Ryan L.
author_facet Elman, Samuel J.
Gavriel, Jason
Mann, Ryan L.
contents We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path decomposition of minimal width. Our analysis shows that approximating the spatial cost of a graph is $\textsf{NP}$-hard, while for graphs with bounded spatial cost, we establish an efficient algorithm for computing an optimal measurement schedule.
format Preprint
id arxiv_https___arxiv_org_abs_2403_04126
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Optimal Scheduling of Graph States via Path Decompositions
Elman, Samuel J.
Gavriel, Jason
Mann, Ryan L.
Quantum Physics
Computational Complexity
Data Structures and Algorithms
We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path decomposition of minimal width. Our analysis shows that approximating the spatial cost of a graph is $\textsf{NP}$-hard, while for graphs with bounded spatial cost, we establish an efficient algorithm for computing an optimal measurement schedule.
title Optimal Scheduling of Graph States via Path Decompositions
topic Quantum Physics
Computational Complexity
Data Structures and Algorithms
url https://arxiv.org/abs/2403.04126