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Area of Science:

  • Quantum computing
  • Artificial intelligence
  • Photonic integrated circuits

Background:

  • Quantum optical neurons (QONs) offer energy-efficient computation via photonic interference.
  • Existing QON architectures require further development for practical applications.

Purpose of the Study:

  • To introduce and evaluate novel QON architectures based on Hong-Ou-Mandel (HOM) and Mach-Zehnder (MZ) interferometers.
  • To assess the performance of QONs with phase, amplitude, and intensity modulation strategies.
  • To investigate QONs' suitability for image classification tasks in both ideal and noisy conditions.

Main Methods:

  • Developed QON architectures using HOM and MZ interferometers with phase, amplitude, and intensity modulation.
  • Implemented QONs as differentiable software modules for neural network integration.
  • Trained multilayer QON networks on MNIST and FashionMNIST datasets for image classification.
  • Evaluated QON performance, accuracy, convergence, and robustness across multiple runs and conditions.

Main Results:

  • MZ-based QONs demonstrated consistent stability, even under noisy conditions.
  • HOM-based QONs with amplitude modulation showed competitive performance in deeper networks, approaching classical results.
  • Phase- and intensity-modulated HOM variants exhibited lower stability and increased sensitivity to perturbations.

Conclusions:

  • QONs show significant potential as efficient and scalable components for quantum-inspired neural networks.
  • MZ interferometers offer robust QON designs suitable for hybrid photonic-electronic systems.
  • Further research into HOM modulation strategies could enhance QON capabilities for complex tasks.