Quantum Machine Learning Applications in High-Energy Physics

Author:

Delgado Andrea1,Hamilton Kathleen E.1

Affiliation:

1. Oak Ridge National Laboratory

Publisher

ACM

Reference21 articles.

1. M. Sohaib Alam et al. 2022. Quantum computing hardware for HEP algorithms and sensing. In 2022 Snowmass Summer Study. arXiv:2204.08605 [quant-ph] M. Sohaib Alam et al. 2022. Quantum computing hardware for HEP algorithms and sensing. In 2022 Snowmass Summer Study. arXiv:2204.08605 [quant-ph]

2. Christian W. Bauer et al . 2022 . Quantum Simulation for High Energy Physics . (4 2022). arXiv:2204.03381 [quant-ph] Christian W. Bauer et al. 2022. Quantum Simulation for High Energy Physics. (4 2022). arXiv:2204.03381 [quant-ph]

3. Marcello Benedetti , Delfina Garcia-Pintos , Oscar Perdomo , Vicente Leyton-Ortega , Yunseong Nam , and Alejandro Perdomo-Ortiz . 2019. A generative modeling approach for benchmarking and training shallow quantum circuits. npj Quantum Inf. 5, 45 ( 2019 ). Marcello Benedetti, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz. 2019. A generative modeling approach for benchmarking and training shallow quantum circuits. npj Quantum Inf. 5, 45 (2019).

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