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Hybrid quantum neural network models for fruit quality assessment.

Danish Ul Khairi1, Kamran Ahsan1, Syed Zeeshan Ali1

  • 1Department of Computer Science, Federal Urdu University of Arts Science and Technology, Karachi, Pakistan.

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|December 10, 2025
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Hybrid quantum neural networks show promise for fruit quality assessment. The controlled-phase (CZ) gate architecture demonstrated more stable training and competitive accuracy compared to controlled-NOT (CNOT) gates.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Hybrid quantum neural networks (HQNNs) are emerging as powerful tools for complex pattern recognition tasks.
  • Assessing fruit quality often requires sophisticated models capable of handling diverse data characteristics.

Purpose of the Study:

  • To investigate the impact of entangling gate choice on HQNN performance for fruit quality assessment.
  • To compare two HQNN architectures: NNQEv1 (CNOT gates) and NNQEv2 (CZ gates).

Main Methods:

  • Developed two HQNN architectures (NNQEv1 and NNQEv2) using different entangling gates (CNOT vs. CZ).
  • Theoretically analyzed architectures based on gate decomposition and hardware noise considerations.
  • Computationally executed quantum circuits on classical hardware for performance evaluation.
  • Compared HQNN models against classical and deep learning benchmarks.

Main Results:

  • Both HQNN models achieved high test accuracies: 98.7% (MNIST), 98.6% (FruitQ), and 96.7% (custom Apple dataset).
  • The CZ-based NNQEv2 exhibited more stable training dynamics and tighter cross-validation confidence intervals than the CNOT-based NNQEv1.
  • Results supported the theoretical prediction of CZ-gate architecture stability.

Conclusions:

  • Gate-level design choices significantly influence HQNN stability and performance.
  • The CZ-gate based HQNN architecture is a promising direction for robust quantum machine learning applications.
  • This study provides foundational insights for developing advanced quantum machine learning algorithms.