Dear Colleagues,

Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim of exploiting the capacity of the hardware for machine learning applications. An interesting question is whether we will be able to develop quantum algorithms that will be able to outperform those classical machine learning algorithms used by the HEP community for decades.

The High Energy Physics community used classical machine learning algorithms to address a wide variety of challenging problems, including searches for the Higgs boson and physics beyond the standard model. This Special Issue aims to gather the latest developments in quantum machine learning algorithms to address challenging problems in particle physics, such as particle classification, track and vertex reconstruction, and physics simulation, beyond the standard model searches and quantum entanglement.

Prof. Dr. Kamal Benslama
Dr. Samuel Yen-Chi Chen
Guest Editors

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• quantum mechanics
• entanglement
• machine learning
• quantum computing
• quantum algorithms
• physics beyond the standard model
• quantum information
• particle physics
• high energy physics
• particle classification
• collider
• neutrino physics
• dark matter
• symmetries in quantum mechanics
• symmetry in physics
• asymmetry
• Bell’s inequality