mlsanityData to 7 Oct 2026

Reservoir Computing Using Measurement-Controlled Quantum Dynamics

arXiv 2403.01024 ↗First posted 1 Mar 2024cs.NE cs.AI quant-phcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 87 references that are in the map. First posted on arXiv 1 Mar 2024.

Builds on

14 of 87 references.

References · 14

  1. 2020Photonics for artificial intelligence and neuromorphic computingcited by 111
  2. 2020Physical reservoir computing -- An introductory perspectivecited by 58
  3. 2021A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardwarecited by 32
  4. 2023A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learningcited by 22
  5. 2019Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitonscited by 19
  6. 2023Analogue and Physical Reservoir Computing Using Water Wavescited by 16
  7. 2022A perspective on physical reservoir computing with nanomagnetic devicescited by 12
  8. 2022A Planck Radiation and Quantization Scheme for Human Cognition and Languagecited by 8
  9. 2022Approximate Computing and the Efficient Machine Learning Expeditioncited by 8
  10. 2023Quantum-Inspired Neural Network Model of Optical Illusionscited by 7
  11. 2021Neural Echo State Network using oscillations of gas bubbles in watercited by 6
  12. 2023A New Paradigm of Reservoir Computing Exploiting Hydrodynamicscited by 5
2 more
  1. 2023Reservoir computing based on solitary-like waves dynamics of film flows: a proof of conceptcited by 5
  2. 2024Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Datacited by 5

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Cited by year

Citing papers per year: 2024: 2; 2025: 5.

7 papers.

  1. 2024Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspectivecited by 9
  2. 2024Quantum-tunnelling deep neural network for optical illusion recognitioncited by 6
  3. 2025Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectivescited by 3
  4. 2025Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regimecited by 2
  5. 2025TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learningcited by 2
  6. 2025Resonant-Tunnelling Diode Reservoir Computing System for Image Recognitioncited by 0
  7. 2025Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series predictioncited by 0