Reservoir Computing Using Measurement-Controlled Quantum Dynamics

Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent d...

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Bibliographic Details
Published in:Electronics (Basel) 2024-03, Vol.13 (6), p.1164
Main Authors: Abbas, A. H., Maksymov, Ivan S.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Comparative analysis
Computation
Computer engineering
Computers
Data mining
Data processing
Digital computers
Electromagnetism
Energy
Energy sources
Forecasts and trends
Information processing
Machine learning
Measurement
Neurons
Quantum theory
Time series
Citations: Items that this one cites
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Summary:Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13061164