Hybrid Quantum Technologies for Quantum Support Vector Machines
Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/2/72 |
| _version_ | 1797297954926624768 |
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| author | Filippo Orazi Simone Gasperini Stefano Lodi Claudio Sartori |
| author_facet | Filippo Orazi Simone Gasperini Stefano Lodi Claudio Sartori |
| author_sort | Filippo Orazi |
| collection | DOAJ |
| description | Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization. |
| first_indexed | 2024-03-07T22:28:47Z |
| format | Article |
| id | doaj.art-9326035c62e943608cfbf3572ec1cc83 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-07T22:28:47Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-9326035c62e943608cfbf3572ec1cc832024-02-23T15:21:01ZengMDPI AGInformation2078-24892024-01-011527210.3390/info15020072Hybrid Quantum Technologies for Quantum Support Vector MachinesFilippo Orazi0Simone Gasperini1Stefano Lodi2Claudio Sartori3Dipartimento di Informatica, Alma Mater Studiorum—University of Bologna, 40126 Bologna, ItalyDipartimento di Informatica, Alma Mater Studiorum—University of Bologna, 40126 Bologna, ItalyDipartimento di Informatica, Alma Mater Studiorum—University of Bologna, 40126 Bologna, ItalyDipartimento di Informatica, Alma Mater Studiorum—University of Bologna, 40126 Bologna, ItalyQuantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.https://www.mdpi.com/2078-2489/15/2/72quantum computingquantum machine learningquantum support vector machinequantum anneallinggate-based quantum computation |
| spellingShingle | Filippo Orazi Simone Gasperini Stefano Lodi Claudio Sartori Hybrid Quantum Technologies for Quantum Support Vector Machines Information quantum computing quantum machine learning quantum support vector machine quantum annealling gate-based quantum computation |
| title | Hybrid Quantum Technologies for Quantum Support Vector Machines |
| title_full | Hybrid Quantum Technologies for Quantum Support Vector Machines |
| title_fullStr | Hybrid Quantum Technologies for Quantum Support Vector Machines |
| title_full_unstemmed | Hybrid Quantum Technologies for Quantum Support Vector Machines |
| title_short | Hybrid Quantum Technologies for Quantum Support Vector Machines |
| title_sort | hybrid quantum technologies for quantum support vector machines |
| topic | quantum computing quantum machine learning quantum support vector machine quantum annealling gate-based quantum computation |
| url | https://www.mdpi.com/2078-2489/15/2/72 |
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