Quantum Fuzzy Inference Engine for Particle Accelerator Control
Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an expone...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10462538/ |
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| _version_ | 1832583985256988672 |
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| author | Giovanni Acampora Michele Grossi Michael Schenk Roberto Schiattarella |
| author_facet | Giovanni Acampora Michele Grossi Michael Schenk Roberto Schiattarella |
| author_sort | Giovanni Acampora |
| collection | DOAJ |
| description | Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications. |
| format | Article |
| id | doaj-art-7e11bb4e963e4c7ca5f69497eca3046f |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-7e11bb4e963e4c7ca5f69497eca3046f2025-01-28T00:02:21ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511310.1109/TQE.2024.337425110462538Quantum Fuzzy Inference Engine for Particle Accelerator ControlGiovanni Acampora0https://orcid.org/0000-0003-4082-5616Michele Grossi1https://orcid.org/0000-0003-1718-1314Michael Schenk2https://orcid.org/0000-0001-9438-812XRoberto Schiattarella3https://orcid.org/0000-0002-1819-2288Department of Physics “Ettore Pancini,”, University of Naples Federico II, Naples, ItalyIT Department, European Organization for Nuclear Research, Meyrin, SwitzerlandBeams Department, European Organization for Nuclear Research, Meyrin, SwitzerlandDepartment of Physics “Ettore Pancini,”, University of Naples Federico II, Naples, ItalyRecently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.https://ieeexplore.ieee.org/document/10462538/Quantum computingfuzzy control systemsparticle accelerators |
| spellingShingle | Giovanni Acampora Michele Grossi Michael Schenk Roberto Schiattarella Quantum Fuzzy Inference Engine for Particle Accelerator Control IEEE Transactions on Quantum Engineering Quantum computing fuzzy control systems particle accelerators |
| title | Quantum Fuzzy Inference Engine for Particle Accelerator Control |
| title_full | Quantum Fuzzy Inference Engine for Particle Accelerator Control |
| title_fullStr | Quantum Fuzzy Inference Engine for Particle Accelerator Control |
| title_full_unstemmed | Quantum Fuzzy Inference Engine for Particle Accelerator Control |
| title_short | Quantum Fuzzy Inference Engine for Particle Accelerator Control |
| title_sort | quantum fuzzy inference engine for particle accelerator control |
| topic | Quantum computing fuzzy control systems particle accelerators |
| url | https://ieeexplore.ieee.org/document/10462538/ |
| work_keys_str_mv | AT giovanniacampora quantumfuzzyinferenceengineforparticleacceleratorcontrol AT michelegrossi quantumfuzzyinferenceengineforparticleacceleratorcontrol AT michaelschenk quantumfuzzyinferenceengineforparticleacceleratorcontrol AT robertoschiattarella quantumfuzzyinferenceengineforparticleacceleratorcontrol |