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AQEA-QAS: An Adaptive Quantum Evolutionary Algorithm for Quantum Architecture Search.

Yaochong Li1,2, Jing Zhang1,2, Rigui Zhou1,2

  • 1College of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.

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Summary
This summary is machine-generated.

This study introduces an adaptive quantum evolution algorithm (AQEA) to optimize quantum neural network (QNN) circuits. The AQEA improves QNN performance by reducing parameters and increasing accuracy, even in noisy quantum computing environments.

Keywords:
quantum computingquantum evolutionary algorithmquantum neural network

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Quantum neural networks (QNNs) leverage quantum computation for enhanced performance over classical networks.
  • Parameterized quantum circuits (PQCs) are crucial for QNNs, but manual or hardware-specific designs often introduce inefficiencies and noise sensitivity.
  • Existing quantum evolutionary algorithms (QEAs) can struggle with local optima due to fixed evolution modes.

Purpose of the Study:

  • To develop an adaptive quantum evolution algorithm (AQEA) for optimizing PQC architectures in QNNs.
  • To address limitations of traditional QEAs, such as local optima and slow convergence.
  • To enhance QNN efficiency, accuracy, and noise resilience.

Main Methods:

  • Introduction of an adaptive mechanism with dual dynamic rotation angles within the evolution process.
  • Implementation of elite retention from parents to offspring to preserve beneficial genetic traits.
  • Inclusion of a quantum catastrophe mechanism to escape local optima during population evolution.

Main Results:

  • The AQEA reduced QNN network parameters by up to 20% compared to manual design and standard QEA.
  • Accuracy was increased by 7.21% using the AQEA-optimized circuits.
  • AQEA-optimized circuits demonstrated superior fidelity and noise resistance in quantum computing environments.

Conclusions:

  • The proposed AQEA effectively optimizes PQC architectures for QNNs, surpassing manual and standard QEA methods.
  • AQEA enhances QNN performance metrics, including parameter count and accuracy.
  • The algorithm's robustness in noisy conditions supports the reliability of quantum computing applications.