Academic Journal
Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
| العنوان: | Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram |
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| المؤلفون: | Sáiz, Álvaro, García Ramos, José Enrique, Arias Carrasco, José Miguel, Lamata, Lucas, Pérez Fernández, Pedro |
| بيانات النشر: | American Physical Society |
| سنة النشر: | 2022 |
| المجموعة: | Universidad de Huelva: Arias Montano |
| مصطلحات موضوعية: | Quantum simulation, Machine learning, Nuclear many-body theory, Phase diagrams, Quantum phase transitions, 22 Física |
| الوصف: | A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich phase diagram with different phases and the corresponding phase transition surfaces. The aim of this work is twofold: on one hand, to propose a quantum simulation of the model at the present limits of the trapped ions facilities and, on the other hand, to show how to use a machine learning algorithm on top of the quantum simulation to accurately determine the phase of the system. Concerning the quantum simulation, this proposal is scalable with polynomial resources to larger Agassi systems. Digital quantum simulations of nuclear physics models assisted by machine learning may enable one to outperform the fastest classical computers in determining fundamental aspects of nuclear matter- ; This work was partially supported by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía (Spain) under Groups No. FQM-160, FQM-177, and FQM-370, and under projects no. P20-00617, P20-00764, P20-01247, UHU-1262561, and US-1380840; by Grants No. PGC2018-095113-B-I00, PID2019-104002GB-C21, PID2019-104002GB-C22, and PID2020-114687GB-I00 funded by MCIN/AEI/10.13039/50110001103 and “ERDF A way of making Europe” and by ERDF, ref. SOMM17/6105/UGR. Resources supporting this work were provided by the CEAFMC and Universidad de Huelva High Performance Computer (HPC@UHU) funded by ERDF/MINECO Project No. UNHU-15CE-2848. ; Ciencias Integradas |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | https://hdl.handle.net/10272/21372 |
| DOI: | 10.1103/physrevc.106.064322 |
| الاتاحة: | https://hdl.handle.net/10272/21372 https://doi.org/10.1103/physrevc.106.064322 |
| Rights: | Atribución-NoComercial-SinDerivadas 3.0 España ; http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ; open access |
| رقم الانضمام: | edsbas.903D6552 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1103/physrevc.106.064322 |
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