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Article: Efficient algorithms for quantum information bottleneck
Title | Efficient algorithms for quantum information bottleneck |
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Authors | |
Issue Date | 2-Mar-2023 |
Publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
Citation | Quantum, 2023, v. 7 How to Cite? |
Abstract | The ability to extract relevant information is critical to learning. An ingenious approach as such is the information bottleneck, an optimi-sation problem whose solution corresponds to a faithful and memory-efficient representation of relevant information from a large system. The advent of the age of quantum computing calls for efficient methods that work on infor-mation regarding quantum systems. Here we address this by proposing a new and general algorithm for the quantum generalisation of in-formation bottleneck. Our algorithm excels in the speed and the definiteness of convergence compared with prior results. It also works for a much broader range of problems, including the quantum extension of deterministic infor-mation bottleneck, an important variant of the original information bottleneck problem. No-tably, we discover that a quantum system can achieve strictly better performance than a clas-sical system of the same size regarding quan-tum information bottleneck, providing new vi-sion on justifying the advantage of quantum machine learning. |
Persistent Identifier | http://hdl.handle.net/10722/331490 |
ISSN | 2023 Impact Factor: 5.1 2023 SCImago Journal Rankings: 2.562 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hayashi, Masahito | - |
dc.contributor.author | Yang, Yuxiang | - |
dc.date.accessioned | 2023-09-21T06:56:17Z | - |
dc.date.available | 2023-09-21T06:56:17Z | - |
dc.date.issued | 2023-03-02 | - |
dc.identifier.citation | Quantum, 2023, v. 7 | - |
dc.identifier.issn | 2521-327X | - |
dc.identifier.uri | http://hdl.handle.net/10722/331490 | - |
dc.description.abstract | <p></p><p>The ability to extract relevant information is critical to learning. An ingenious approach as such is the information bottleneck, an optimi-sation problem whose solution corresponds to a faithful and memory-efficient representation of relevant information from a large system. The advent of the age of quantum computing calls for efficient methods that work on infor-mation regarding quantum systems. Here we address this by proposing a new and general algorithm for the quantum generalisation of in-formation bottleneck. Our algorithm excels in the speed and the definiteness of convergence compared with prior results. It also works for a much broader range of problems, including the quantum extension of deterministic infor-mation bottleneck, an important variant of the original information bottleneck problem. No-tably, we discover that a quantum system can achieve strictly better performance than a clas-sical system of the same size regarding quan-tum information bottleneck, providing new vi-sion on justifying the advantage of quantum machine learning.<br></p> | - |
dc.language | eng | - |
dc.publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften | - |
dc.relation.ispartof | Quantum | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Efficient algorithms for quantum information bottleneck | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.22331/q-2023-03-02-936 | - |
dc.identifier.scopus | eid_2-s2.0-85159327784 | - |
dc.identifier.volume | 7 | - |
dc.identifier.eissn | 2521-327X | - |
dc.identifier.isi | WOS:000958945600001 | - |
dc.identifier.issnl | 2521-327X | - |