Graph algorithms with neutral atom quantum processors
Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from s...
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| Published in | The European physical journal. A, Hadrons and nuclei Vol. 60; no. 9; p. 177 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
06.09.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1434-601X 1434-6001 1434-601X |
| DOI | 10.1140/epja/s10050-024-01385-5 |
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| Abstract | Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from shot to shot. This unique feature makes possible the native embedding of graph-structured problems at the hardware level, with profound consequences for the resolution of complex optimization and machine learning tasks. By driving qubits, one can generate processed quantum states which retain graph complex properties. These states can then be leveraged to offer direct solutions to problems or as resources in hybrid quantum-classical schemes. In this paper, we review the advancements in quantum algorithms for graph problems running on neutral atom Quantum Processing Units (QPUs), and discuss recently introduced embedding and problem-solving techniques. In addition, we clarify ongoing advancements in hardware, with an emphasis on enhancing the scalability, controllability and computation repetition rate of neutral atom QPUs. |
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| AbstractList | Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from shot to shot. This unique feature makes possible the native embedding of graph-structured problems at the hardware level, with profound consequences for the resolution of complex optimization and machine learning tasks. By driving qubits, one can generate processed quantum states which retain graph complex properties. These states can then be leveraged to offer direct solutions to problems or as resources in hybrid quantum-classical schemes. In this paper, we review the advancements in quantum algorithms for graph problems running on neutral atom Quantum Processing Units (QPUs), and discuss recently introduced embedding and problem-solving techniques. In addition, we clarify ongoing advancements in hardware, with an emphasis on enhancing the scalability, controllability and computation repetition rate of neutral atom QPUs. |
| ArticleNumber | 177 |
| Author | Djellabi, Mehdi Henriet, Loïc Ximenez, Bruno Dareau, Alexandre Dalyac, Constantin Leclerc, Lucas Vignoli, Louis Signoles, Adrien Dreon, Davide Elfving, Vincent E. Coelho, Wesley da Silva Henry, Louis-Paul |
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| CitedBy_id | crossref_primary_10_1103_PhysRevResearch_6_043020 crossref_primary_10_1103_PRXQuantum_6_010301 crossref_primary_10_1103_PhysRevA_111_032611 |
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| Copyright_xml | – notice: The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. |
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| Snippet | Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for... |
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| SubjectTerms | Algorithms Cognitive tasks Electrons Embedding Graphs Hadrons Hardware Heavy Ions Machine learning Neutral atoms Nuclear Fusion Nuclear Physics Optimization Particle and Nuclear Physics Physics Physics and Astronomy Problem solving Quantum Computing in Low-Energy Nuclear Theory Qubits (quantum computing) Review Task complexity |
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