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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Natural quantum reservoir computing for temporal information processing.

Yudai Suzuki1, Qi Gao2,3, Ken C Pradel2

  • 1Department of Mechanical Engineering, Keio University, Hiyoshi 3-14-1, Kohoku, Yokohama, 223-8522, Japan. yudai.suzuki.sh@gmail.com.

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This study demonstrates that noisy superconducting quantum computing devices can function as effective reservoir computers. Quantum noise, typically a hindrance, is repurposed as a valuable computational resource for time-series processing.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Complex Systems

Background:

  • Reservoir computing leverages dissipative dynamics for temporal information processing.
  • Conventional quantum computation aims to minimize noise, viewing it as detrimental.

Purpose of the Study:

  • To investigate the feasibility of using real superconducting quantum computing devices as reservoirs.
  • To explore the potential of utilizing inherent quantum noise as a computational resource.

Main Methods:

  • Implementation of a superconducting quantum computing device as a reservoir.
  • Demonstration on a benchmark time-series regression task.
  • Application to a practical temporal sensor data classification problem.

Main Results:

  • The quantum reservoir computer outperformed linear models in both benchmark and practical tasks.
  • Natural quantum noise was effectively utilized as a dissipative element for computation.
  • The proposed method showed higher performance compared to traditional linear regression and classification.

Conclusions:

  • Noisy quantum devices can serve as effective reservoir computers.
  • Quantum noise can be repurposed as a valuable computational resource, contrary to conventional approaches.
  • This opens new avenues for quantum-enhanced machine learning and temporal data processing.