Reversible quantum communication & systems. Issue 1 (15th March 2022)

• Record Type: Journal Article
• Authors: Sengupta, Diganta; Abd El‐Latif, Ahmed; De, Debashis; Navi, Keivan; Bagherzadeh, Nader
• Other Names: Sengupta Diganta guestEditor.; Abd El‐Latif Ahmed guestEditor.; De Debashis guestEditor.; Navi Keivan guestEditor.; Bagherzadeh Nader guestEditor.
• Is Part Of: IET quantum communication. Volume 3:Issue 1(2022)
• Page Start: 1
• Page End: 4
• Publication Date: 2022-03-15
• Subjects: Quantum communication -- Periodicals; Quantum communication; Periodicals; 004.6
• DOI: 10.1049/qtc2.12037
• Languages: English
• ISSNs: 2632-8925

Abstract

Abstract: Quantum Computing has emerged as one of the important dimensions of global research lately, on both the prospects, hardware as well as algorithms. With enhanced processing powers, several architectures based on adiabatic concepts resulting in reversibility have been proposed to date. Architectures based on Quantum Dot Cellular Automata have also shown considerable promise for realising the concept of reversibility. Recently, research has been focussed on the application of quantum computing for faster and secure communication. Dedicated machine learning algorithms and neural networks for quantum computation have also attracted considerable research. With a plethora of research and advances in this domain, this Special Issue publishes outstanding contributions for dissemination of the knowledge of Reversible Quantum Communication & Systems. This Special Issue publishes latest approaches and findings in Quantum Algorithms and Reversible Computing with focus on emerging Machine Learning approaches in Quantum Communications. Reversible Logic forms a pivotal part of Quantum Computing and has been a topic of high interest among Quantum Computing Scientists and researchers throughout the last decade. It also exhibits considerable prospects in recent research due to its adiabatic characteristics. Logic synthesis and optimisation algorithms within the purview of Reversibility have witnessed credible approaches and pose future prospects, such as the rise of Machine Learning approaches which have also penetrated the Quantum Domain.