تقرير
Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
| العنوان: | Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning |
|---|---|
| المؤلفون: | García-Ramos, J. E., Sáiz, A., Arias, J. M., Lamata, L., Pérez-Fernández, P. |
| سنة النشر: | 2023 |
| المجموعة: | Nuclear Experiment Nuclear Theory Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Nuclear Experiment, Nuclear Theory |
| الوصف: | In this paper, the application of quantum simulations and quantum machine learning to solve low-energy nuclear physics problems is explored. The use of quantum computing to deal with nuclear physics problems is, in general, in its infancy and, in particular, the use of quantum machine learning in the realm of nuclear physics at low energy is almost nonexistent. We present here three specific examples where the use of quantum computing and quantum machine learning provides, or could provide in the future, a possible computational advantage: i) the determination of the phase/shape in schematic nuclear models, ii) the calculation of the ground state energy of a nuclear shell model-type Hamiltonian and iii) the identification of particles or the determination of trajectories in nuclear physics experiments. Comment: Submitted to the special issue "Quantum Machine Learning" of the journal Advanced Quantum Technologies |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2307.07332 |
| رقم الأكسشن: | edsarx.2307.07332 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |