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Quantum Neuromorphic Computing with Reservoir Computing Networks
Author(s) -
Ghosh Sanjib,
Nakajima Kohei,
Krisnanda Tanjung,
Fujii Keisuke,
Liew Timothy C. H.
Publication year - 2021
Publication title -
advanced quantum technologies
Language(s) - English
Resource type - Journals
ISSN - 2511-9044
DOI - 10.1002/qute.202100053
Subject(s) - quantum computer , reservoir computing , neuromorphic engineering , computer science , quantum , quantum network , quantum information , quantum technology , artificial neural network , theoretical computer science , computational science , open quantum system , physics , quantum mechanics , artificial intelligence , recurrent neural network
Abstract Quantum reservoir networks combine the intelligence of neural networks with the potential of quantum computing in a single platform. This platform operates on the architecture of reservoir computing, which can function even with random connections between neural nodes. This is a major advantage for hardware implementation. Herein is described how reservoir computing is brought into the quantum domain to perform various tasks, including characterization of quantum states, quantum estimation, quantum state preparation, and quantum computing. It shows quantum enhancement in classical data processing, and creates the opportunity for quantum information processing within the robust paradigm of neural networks. It is friendly for implementation in a wide range of physical systems, including quantum dots, superconductors, trapped ions, cold atoms, and exciton‐polaritons.