Applications of Quantum Annealing & Computation in Machine Learning, Neural Networks and Consciousness Modelling
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Physics".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 290
Special Issue Editor
Special Issue Information
Dear Colleagues,
Following developments in our understanding of the stochastic methods of analog quantum computing using quantum annealing, whereby quantum tunneling helps in escaping tall but thin local cost function barriers (inducing Replica Symmetry of the rugged cost function landscape), major developments in quantum annealers have taken place in the last two decades. While basic quantum physics and technology are still in progress, some major applications of quantum annealing computation have occurred in the last five years or so thanks to the availability of practical quantum annealers on the market (e.g., D-Wave machines). Of particular note is the intense activity in the search for quantum advantages in the application of quantum annealers to machine learning for classification, to deep neural networks, to the robustness of hybrid quantum-classical neural networks, to quantum information models of consciousness, etc. This Special Issue invites mathematicians, physicists, computer scientists, engineers and software specialists to share new research results in these directions, as well as comprehensive reviews, to introduce younger researchers in this exciting field to real-world problems in the application of quantum annealing computation.
Prof. Dr. Bikas K. Chakrabarti
Guest Editor
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