Quantum Autoencoders for Learning Quantum Channel Codes
This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum cha...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Rathi, Lakshika DiAdamo, Stephen Shabani, Alireza |
| description | This work investigates the application of quantum machine learning techniques
for classical and quantum communication across different qubit channel models.
By employing parameterized quantum circuits and a flexible channel noise model,
we develop a machine learning framework to generate quantum channel codes and
evaluate their effectiveness. We explore classical, entanglement-assisted, and
quantum communication scenarios within our framework. Applying it to various
quantum channel models as proof of concept, we demonstrate strong performance
in each case. Our results highlight the potential of quantum machine learning
in advancing research on quantum communication systems, enabling a better
understanding of capacity bounds under modulation constraints, various
communication settings, and diverse channel models. |
| doi_str_mv | 10.48550/arxiv.2307.06622 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2307_06622</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2307_06622</sourcerecordid><originalsourceid>FETCH-LOGICAL-a672-de368b5c734d722267ff58ceca5582d9a6c4d1a8f62eb6d5a2886e8a7c978bba3</originalsourceid><addsrcrecordid>eNo1j0FOwzAQRb3pArUcgBW-QEI6jsfTZRVRQIqEkLqPJvYYIrUOchpUbg8UWP3N09d7St2sq7Ima6s7zufhowRTubJCBLhS7mXmdJqPejufRkl-DJInHcesW-GchvSq_4nmjVOSg26-mWmlFpEPk1z_7VLtd_f75rFonx-emm1bMDooghik3npn6uAAAF2Mlrx4tpYgbBh9HdZMEUF6DJaBCIXY-Y2jvmezVLe_txfz7j0PR86f3U9BdykwX2sfQQY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Quantum Autoencoders for Learning Quantum Channel Codes</title><source>arXiv.org</source><creator>Rathi, Lakshika ; DiAdamo, Stephen ; Shabani, Alireza</creator><creatorcontrib>Rathi, Lakshika ; DiAdamo, Stephen ; Shabani, Alireza</creatorcontrib><description>This work investigates the application of quantum machine learning techniques
for classical and quantum communication across different qubit channel models.
By employing parameterized quantum circuits and a flexible channel noise model,
we develop a machine learning framework to generate quantum channel codes and
evaluate their effectiveness. We explore classical, entanglement-assisted, and
quantum communication scenarios within our framework. Applying it to various
quantum channel models as proof of concept, we demonstrate strong performance
in each case. Our results highlight the potential of quantum machine learning
in advancing research on quantum communication systems, enabling a better
understanding of capacity bounds under modulation constraints, various
communication settings, and diverse channel models.</description><identifier>DOI: 10.48550/arxiv.2307.06622</identifier><language>eng</language><subject>Computer Science - Information Theory ; Computer Science - Learning ; Mathematics - Information Theory ; Physics - Quantum Physics</subject><creationdate>2023-07</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2307.06622$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2307.06622$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Rathi, Lakshika</creatorcontrib><creatorcontrib>DiAdamo, Stephen</creatorcontrib><creatorcontrib>Shabani, Alireza</creatorcontrib><title>Quantum Autoencoders for Learning Quantum Channel Codes</title><description>This work investigates the application of quantum machine learning techniques
for classical and quantum communication across different qubit channel models.
By employing parameterized quantum circuits and a flexible channel noise model,
we develop a machine learning framework to generate quantum channel codes and
evaluate their effectiveness. We explore classical, entanglement-assisted, and
quantum communication scenarios within our framework. Applying it to various
quantum channel models as proof of concept, we demonstrate strong performance
in each case. Our results highlight the potential of quantum machine learning
in advancing research on quantum communication systems, enabling a better
understanding of capacity bounds under modulation constraints, various
communication settings, and diverse channel models.</description><subject>Computer Science - Information Theory</subject><subject>Computer Science - Learning</subject><subject>Mathematics - Information Theory</subject><subject>Physics - Quantum Physics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNo1j0FOwzAQRb3pArUcgBW-QEI6jsfTZRVRQIqEkLqPJvYYIrUOchpUbg8UWP3N09d7St2sq7Ima6s7zufhowRTubJCBLhS7mXmdJqPejufRkl-DJInHcesW-GchvSq_4nmjVOSg26-mWmlFpEPk1z_7VLtd_f75rFonx-emm1bMDooghik3npn6uAAAF2Mlrx4tpYgbBh9HdZMEUF6DJaBCIXY-Y2jvmezVLe_txfz7j0PR86f3U9BdykwX2sfQQY</recordid><startdate>20230713</startdate><enddate>20230713</enddate><creator>Rathi, Lakshika</creator><creator>DiAdamo, Stephen</creator><creator>Shabani, Alireza</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20230713</creationdate><title>Quantum Autoencoders for Learning Quantum Channel Codes</title><author>Rathi, Lakshika ; DiAdamo, Stephen ; Shabani, Alireza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-de368b5c734d722267ff58ceca5582d9a6c4d1a8f62eb6d5a2886e8a7c978bba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Information Theory</topic><topic>Computer Science - Learning</topic><topic>Mathematics - Information Theory</topic><topic>Physics - Quantum Physics</topic><toplevel>online_resources</toplevel><creatorcontrib>Rathi, Lakshika</creatorcontrib><creatorcontrib>DiAdamo, Stephen</creatorcontrib><creatorcontrib>Shabani, Alireza</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rathi, Lakshika</au><au>DiAdamo, Stephen</au><au>Shabani, Alireza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantum Autoencoders for Learning Quantum Channel Codes</atitle><date>2023-07-13</date><risdate>2023</risdate><abstract>This work investigates the application of quantum machine learning techniques
for classical and quantum communication across different qubit channel models.
By employing parameterized quantum circuits and a flexible channel noise model,
we develop a machine learning framework to generate quantum channel codes and
evaluate their effectiveness. We explore classical, entanglement-assisted, and
quantum communication scenarios within our framework. Applying it to various
quantum channel models as proof of concept, we demonstrate strong performance
in each case. Our results highlight the potential of quantum machine learning
in advancing research on quantum communication systems, enabling a better
understanding of capacity bounds under modulation constraints, various
communication settings, and diverse channel models.</abstract><doi>10.48550/arxiv.2307.06622</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2307.06622 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2307_06622 |
| source | arXiv.org |
| subjects | Computer Science - Information Theory Computer Science - Learning Mathematics - Information Theory Physics - Quantum Physics |
| title | Quantum Autoencoders for Learning Quantum Channel Codes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T02%3A14%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Quantum%20Autoencoders%20for%20Learning%20Quantum%20Channel%20Codes&rft.au=Rathi,%20Lakshika&rft.date=2023-07-13&rft_id=info:doi/10.48550/arxiv.2307.06622&rft_dat=%3Carxiv_GOX%3E2307_06622%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |