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Quantum coherence is key to quantum computation supremacy. This study shows quantum coherence distribution and depletion are crucial for training variational quantum perceptrons, enhancing quantum algorithm performance.

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Area of Science:

  • Quantum Information Science
  • Quantum Computation
  • Machine Learning

Background:

  • Quantum coherence is a vital resource for quantum algorithms and quantum computation.
  • Variational quantum algorithms are a promising approach for near-term quantum computers.
  • Understanding the role of quantum resources in variational algorithms is essential for their development.

Purpose of the Study:

  • To investigate the contribution of quantum coherence to the training of variational quantum perceptrons.
  • To analyze the distribution and consumption of quantum coherence during the training process.
  • To examine the behavior of entanglement, specifically bipartite concurrence, during variational quantum perceptron training.

Main Methods:

  • Analysis of quantum coherence dynamics within the variational quantum perceptron.
  • Application of the Grover algorithm to the index register during training.
  • Calculation of bipartite concurrence to quantify entanglement between registers and within the index register.

Main Results:

  • Quantum coherence concentrates in the index register during the initial training phase.
  • The Grover algorithm consumes quantum coherence from the index register in the later training phase.
  • Bipartite concurrence between feature and index registers decreases, while concurrence within the index register increases.

Conclusions:

  • Quantum coherence distribution and depletion are necessary for effective variational quantum perceptron training.
  • The observed entanglement dynamics reflect the specific operations performed on the index register.
  • These findings provide insights into the interplay of quantum resources in variational quantum machine learning.