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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Information Scrambling in Quantum Neural Networks.

Huitao Shen1, Pengfei Zhang2,3,4, Yi-Zhuang You5

  • 1Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

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Quantum neural networks (QNNs) can be understood through information scrambling. Analyzing tripartite information reveals a two-stage training process in QNNs, correlating with performance improvements and information dynamics.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Information Theory

Background:

  • Quantum neural networks (QNNs) are a promising application for noisy intermediate-scale quantum computers.
  • QNNs process information by distilling input wave functions into output qubits.

Purpose of the Study:

  • To reframe QNN information processing as quantum information scrambling from output to input.
  • To utilize tripartite information, a measure of information scrambling, to analyze QNN training dynamics.

Main Methods:

  • Viewing QNN information flow in reverse: from output qubits to input.
  • Applying tripartite information to diagnose the training process of QNNs.
  • Empirically correlating tripartite information dynamics with the loss function during training.

Main Results:

  • A strong correlation was found between tripartite information behavior and the loss function.
  • QNN training exhibits two distinct stages for randomly initialized networks.
  • Early stage: rapid performance improvement, linear increase in tripartite information (less scrambling).
  • Later stage: slow performance improvement, decrease in tripartite information (local correlations to large-scale structures).

Conclusions:

  • The two-stage training dynamic, characterized by information scrambling, is likely universal for QNNs.
  • This approach provides a new perspective for understanding QNNs by bridging quantum computing and information scrambling research.