Quantum reservoir processing

• Main Authors: Ghosh, Sanjib, Opala, Andrzej, Matuszewski, Michał, Paterek, Tomasz, Liew, Timothy C. H.
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2019
• Subjects: DRNTU::Science::Physics; Quantum Neural Network; Quantum Information Processing
• Online Access: https://hdl.handle.net/10356/104750; http://hdl.handle.net/10220/48758; https://doi.org/10.21979/N9/VIMXR5
• Institution: Nanyang Technological University

The concurrent rise of artificial intelligence and quantum information poses an opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of an artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a nonlinear function of an input quantum state (e.g., entropy, purity, or logarithmic negativity). In this way, experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.