Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer

Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a qua...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports Jg. 12; H. 1; S. 17171 - 14
Hauptverfasser: Niroula, Pradeep, Shaydulin, Ruslan, Yalovetzky, Romina, Minssen, Pierre, Herman, Dylan, Hu, Shaohan, Pistoia, Marco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 13.10.2022
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/117
639/766/259
639/766/483/481
Algorithms
computer science
Computers
Humanities and Social Sciences
information theory and computation
MATHEMATICS AND COMPUTING
multidisciplinary
Optimization algorithms
Quantum computing
quantum information
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Realizing the potential of near-term quantum computers to solve industry-relevant constrained-optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on trapped-ion quantum computer. We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 20 qubits and a two-qubit gate depth of up to 159. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY-QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. We discuss the respective trade-offs of the algorithms and implications for their execution on near-term quantum hardware.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
SC0019040; SC0019499; SC0020312; OMA-2120757
USDOE
USDOE Office of Science (SC)
National Science Foundation (NSF)
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-20853-w