Computational Methods for Quantum Matter
A special issue of Condensed Matter (ISSN 2410-3896). This special issue belongs to the section "Quantum Materials".
Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 30725
Special Issue Editors
Interests: ultracold atoms; computational physics
Interests: ultracold clouds
Special Issue Information
Dear Colleagues,
In recent years, remarkable experimental breakthroughs achieved with platforms such as atomic gases, trapped ions, and superconducting qubits have allowed for controlled realizations of various intriguing quantum phenomena. These experiments represent the ideal venue to test novel computational methods for quantum many-body systems. Indeed, various computational frameworks are being developed beyond the state of the art, including, among others: quantum Monte Carlo algorithms, diagrammatic expansions, exact-diagonalization computations, tensor network methods, and density functional theories. Furthermore, machine learning models, e.g., deep neural networks, are currently being imported into the domain of quantum simulations.
This Special Issue aims to showcase relevant computational studies on quantum matter, highlighting successful applications, recent algorithmic advancements, and also the main open challenges. Contributing authors are invited to elucidate physics insights provided by the employed computational techniques and/or to describe enabling technical developments. The further goals are also to provide the community with helpful information for future computational advancements and to bring the open challenges to be addressed by quantum many-body physicists in the near and the midterm future into the spotlight.
Dr. Sebastiano Pilati
Prof. Dr. Bruno Julia-Diaz
Prof. Dr. Stefano Giorgini
Guest Editors
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