Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA Article Swipe
Lorenzo Borella
,
Alberto Coppi
,
J. Pazzini
,
Andrea Stanco
,
Marco Trenti
,
Andrea Triossi
,
Marco Zanetti
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.16075
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.16075
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learning techniques. In this work, we study the use of Tree Tensor Networks (TTNs) in high-frequency real-time applications by exploiting the low-latency hardware of the Field-Programmable Gate Array (FPGA) technology. We present different implementations of TTN classifiers, capable of performing inference on classical ML datasets as well as on complex physics data. A preparatory analysis of bond dimensions and weight quantization is realized in the training phase, together with entanglement entropy and correlation measurements, that help setting the choice of the TTN architecture. The generated TTNs are then deployed on a hardware accelerator; using an FPGA integrated into a server, the inference of the TTN is completely offloaded. Eventually, a classifier for High Energy Physics (HEP) applications is implemented and executed fully pipelined with sub-microsecond latency.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.16075
- https://arxiv.org/pdf/2409.16075
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403788995
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403788995Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2409.16075Digital Object Identifier
- Title
-
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGAWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-24Full publication date if available
- Authors
-
Lorenzo Borella, Alberto Coppi, J. Pazzini, Andrea Stanco, Marco Trenti, Andrea Triossi, Marco ZanettiList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.16075Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2409.16075Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2409.16075Direct OA link when available
- Concepts
-
Field-programmable gate array, Latency (audio), Computer science, Low latency (capital markets), Quantum, Embedded system, Computer network, Telecommunications, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403788995 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2409.16075 |
| ids.doi | https://doi.org/10.48550/arxiv.2409.16075 |
| ids.openalex | https://openalex.org/W4403788995 |
| fwci | |
| type | preprint |
| title | Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10682 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.988099992275238 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Quantum Computing Algorithms and Architecture |
| topics[1].id | https://openalex.org/T10558 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9811999797821045 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Advancements in Semiconductor Devices and Circuit Design |
| topics[2].id | https://openalex.org/T13182 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9710000157356262 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1703 |
| topics[2].subfield.display_name | Computational Theory and Mathematics |
| topics[2].display_name | Quantum-Dot Cellular Automata |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C42935608 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8181161880493164 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q190411 |
| concepts[0].display_name | Field-programmable gate array |
| concepts[1].id | https://openalex.org/C82876162 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7279129028320312 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q17096504 |
| concepts[1].display_name | Latency (audio) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6862759590148926 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C46637626 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6569409370422363 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q6693015 |
| concepts[3].display_name | Low latency (capital markets) |
| concepts[4].id | https://openalex.org/C84114770 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4756483733654022 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q46344 |
| concepts[4].display_name | Quantum |
| concepts[5].id | https://openalex.org/C149635348 |
| concepts[5].level | 1 |
| concepts[5].score | 0.40699639916419983 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q193040 |
| concepts[5].display_name | Embedded system |
| concepts[6].id | https://openalex.org/C31258907 |
| concepts[6].level | 1 |
| concepts[6].score | 0.11896967887878418 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[6].display_name | Computer network |
| concepts[7].id | https://openalex.org/C76155785 |
| concepts[7].level | 1 |
| concepts[7].score | 0.08783954381942749 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[7].display_name | Telecommunications |
| concepts[8].id | https://openalex.org/C121332964 |
| concepts[8].level | 0 |
| concepts[8].score | 0.08595547080039978 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[8].display_name | Physics |
| concepts[9].id | https://openalex.org/C62520636 |
| concepts[9].level | 1 |
| concepts[9].score | 0.052629321813583374 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[9].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/field-programmable-gate-array |
| keywords[0].score | 0.8181161880493164 |
| keywords[0].display_name | Field-programmable gate array |
| keywords[1].id | https://openalex.org/keywords/latency |
| keywords[1].score | 0.7279129028320312 |
| keywords[1].display_name | Latency (audio) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6862759590148926 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/low-latency |
| keywords[3].score | 0.6569409370422363 |
| keywords[3].display_name | Low latency (capital markets) |
| keywords[4].id | https://openalex.org/keywords/quantum |
| keywords[4].score | 0.4756483733654022 |
| keywords[4].display_name | Quantum |
| keywords[5].id | https://openalex.org/keywords/embedded-system |
| keywords[5].score | 0.40699639916419983 |
| keywords[5].display_name | Embedded system |
| keywords[6].id | https://openalex.org/keywords/computer-network |
| keywords[6].score | 0.11896967887878418 |
| keywords[6].display_name | Computer network |
| keywords[7].id | https://openalex.org/keywords/telecommunications |
| keywords[7].score | 0.08783954381942749 |
| keywords[7].display_name | Telecommunications |
| keywords[8].id | https://openalex.org/keywords/physics |
| keywords[8].score | 0.08595547080039978 |
| keywords[8].display_name | Physics |
| keywords[9].id | https://openalex.org/keywords/quantum-mechanics |
| keywords[9].score | 0.052629321813583374 |
| keywords[9].display_name | Quantum mechanics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2409.16075 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2409.16075 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2409.16075 |
| locations[1].id | doi:10.48550/arxiv.2409.16075 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2409.16075 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5114415482 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Lorenzo Borella |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Borella, Lorenzo |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5112295180 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Alberto Coppi |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Coppi, Alberto |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5039937730 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1118-6205 |
| authorships[2].author.display_name | J. Pazzini |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Pazzini, Jacopo |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5031926716 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8442-9055 |
| authorships[3].author.display_name | Andrea Stanco |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Stanco, Andrea |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5008113416 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Marco Trenti |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Trenti, Marco |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5006259128 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-5140-9154 |
| authorships[5].author.display_name | Andrea Triossi |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Triossi, Andrea |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5107233188 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Marco Zanetti |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Zanetti, Marco |
| authorships[6].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2409.16075 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-10-27T00:00:00 |
| display_name | Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10682 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.988099992275238 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Quantum Computing Algorithms and Architecture |
| related_works | https://openalex.org/W2096844293, https://openalex.org/W2363944576, https://openalex.org/W2351041855, https://openalex.org/W2570254841, https://openalex.org/W3205411230, https://openalex.org/W4286899009, https://openalex.org/W9168048, https://openalex.org/W4300849822, https://openalex.org/W4376480820, https://openalex.org/W3155891479 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2409.16075 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2409.16075 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2409.16075 |
| primary_location.id | pmh:oai:arXiv.org:2409.16075 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2409.16075 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2409.16075 |
| publication_date | 2024-09-24 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 87 |
| abstract_inverted_index.a | 4, 125, 133, 144 |
| abstract_inverted_index.In | 37 |
| abstract_inverted_index.ML | 78 |
| abstract_inverted_index.We | 65 |
| abstract_inverted_index.an | 129 |
| abstract_inverted_index.as | 80, 82 |
| abstract_inverted_index.be | 21 |
| abstract_inverted_index.by | 53 |
| abstract_inverted_index.in | 49, 98 |
| abstract_inverted_index.is | 96, 140, 152 |
| abstract_inverted_index.of | 44, 58, 69, 73, 90, 114, 137 |
| abstract_inverted_index.on | 76, 83, 124 |
| abstract_inverted_index.to | 23, 32 |
| abstract_inverted_index.we | 40 |
| abstract_inverted_index.TNs | 18 |
| abstract_inverted_index.TTN | 70, 116, 139 |
| abstract_inverted_index.The | 118 |
| abstract_inverted_index.and | 93, 106, 154 |
| abstract_inverted_index.are | 3, 121 |
| abstract_inverted_index.can | 19 |
| abstract_inverted_index.for | 8, 146 |
| abstract_inverted_index.how | 17 |
| abstract_inverted_index.the | 42, 55, 59, 99, 112, 115, 135, 138 |
| abstract_inverted_index.use | 43 |
| abstract_inverted_index.(ML) | 27 |
| abstract_inverted_index.FPGA | 130 |
| abstract_inverted_index.Gate | 61 |
| abstract_inverted_index.High | 147 |
| abstract_inverted_index.TTNs | 120 |
| abstract_inverted_index.Tree | 45 |
| abstract_inverted_index.also | 20 |
| abstract_inverted_index.bond | 91 |
| abstract_inverted_index.have | 15 |
| abstract_inverted_index.help | 110 |
| abstract_inverted_index.into | 132 |
| abstract_inverted_index.that | 109 |
| abstract_inverted_index.then | 122 |
| abstract_inverted_index.this | 38 |
| abstract_inverted_index.used | 7 |
| abstract_inverted_index.well | 81 |
| abstract_inverted_index.with | 103, 158 |
| abstract_inverted_index.(HEP) | 150 |
| abstract_inverted_index.(TNs) | 2 |
| abstract_inverted_index.Array | 62 |
| abstract_inverted_index.data. | 86 |
| abstract_inverted_index.fully | 156 |
| abstract_inverted_index.shown | 16 |
| abstract_inverted_index.study | 41 |
| abstract_inverted_index.using | 128 |
| abstract_inverted_index.work, | 39 |
| abstract_inverted_index.works | 14 |
| abstract_inverted_index.(FPGA) | 63 |
| abstract_inverted_index.(TTNs) | 48 |
| abstract_inverted_index.Energy | 148 |
| abstract_inverted_index.Recent | 13 |
| abstract_inverted_index.Tensor | 0, 46 |
| abstract_inverted_index.choice | 113 |
| abstract_inverted_index.phase, | 101 |
| abstract_inverted_index.tasks, | 28 |
| abstract_inverted_index.weight | 94 |
| abstract_inverted_index.Machine | 25 |
| abstract_inverted_index.Physics | 149 |
| abstract_inverted_index.applied | 22 |
| abstract_inverted_index.capable | 72 |
| abstract_inverted_index.complex | 84 |
| abstract_inverted_index.entropy | 105 |
| abstract_inverted_index.perform | 24 |
| abstract_inverted_index.physics | 85 |
| abstract_inverted_index.present | 66 |
| abstract_inverted_index.quantum | 10 |
| abstract_inverted_index.results | 31 |
| abstract_inverted_index.server, | 134 |
| abstract_inverted_index.setting | 111 |
| abstract_inverted_index.Learning | 26 |
| abstract_inverted_index.Networks | 1, 47 |
| abstract_inverted_index.analysis | 89 |
| abstract_inverted_index.datasets | 79 |
| abstract_inverted_index.deployed | 123 |
| abstract_inverted_index.executed | 155 |
| abstract_inverted_index.hardware | 57, 126 |
| abstract_inverted_index.latency. | 160 |
| abstract_inverted_index.learning | 35 |
| abstract_inverted_index.paradigm | 6 |
| abstract_inverted_index.realized | 97 |
| abstract_inverted_index.standard | 33 |
| abstract_inverted_index.systems. | 12 |
| abstract_inverted_index.together | 102 |
| abstract_inverted_index.training | 100 |
| abstract_inverted_index.yielding | 29 |
| abstract_inverted_index.classical | 77 |
| abstract_inverted_index.different | 67 |
| abstract_inverted_index.generated | 119 |
| abstract_inverted_index.inference | 75, 136 |
| abstract_inverted_index.many-body | 11 |
| abstract_inverted_index.pipelined | 157 |
| abstract_inverted_index.real-time | 51 |
| abstract_inverted_index.classifier | 145 |
| abstract_inverted_index.comparable | 30 |
| abstract_inverted_index.completely | 141 |
| abstract_inverted_index.dimensions | 92 |
| abstract_inverted_index.exploiting | 54 |
| abstract_inverted_index.integrated | 131 |
| abstract_inverted_index.offloaded. | 142 |
| abstract_inverted_index.performing | 74 |
| abstract_inverted_index.supervised | 34 |
| abstract_inverted_index.Eventually, | 143 |
| abstract_inverted_index.correlation | 107 |
| abstract_inverted_index.implemented | 153 |
| abstract_inverted_index.low-latency | 56 |
| abstract_inverted_index.preparatory | 88 |
| abstract_inverted_index.techniques. | 36 |
| abstract_inverted_index.technology. | 64 |
| abstract_inverted_index.accelerator; | 127 |
| abstract_inverted_index.applications | 52, 151 |
| abstract_inverted_index.classifiers, | 71 |
| abstract_inverted_index.entanglement | 104 |
| abstract_inverted_index.quantization | 95 |
| abstract_inverted_index.representing | 9 |
| abstract_inverted_index.architecture. | 117 |
| abstract_inverted_index.computational | 5 |
| abstract_inverted_index.measurements, | 108 |
| abstract_inverted_index.high-frequency | 50 |
| abstract_inverted_index.implementations | 68 |
| abstract_inverted_index.sub-microsecond | 159 |
| abstract_inverted_index.Field-Programmable | 60 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |