Model Architecture

Detailed structure of the ZPE Quantum Neural Network

Network Summary
High-level overview of the ZPE neural network

Convolutional Backbone

Four convolutional layers with increasing channel dimensions (64-128-256-512) providing hierarchical feature extraction. Each layer includes batch normalization, GELU activation, and SE blocks.

ZPE Flow Integration

Zero-Point Energy flow applied after each layer with dynamically adjusted parameters. Flow momentum, strength, noise, and coupling are fine-tuned per layer to optimize performance.

Skip Connections

Residual connections between layers enable better gradient flow and information preservation. Each skip connection includes a 1×1 convolution for dimension matching.

Quantum Noise Injection

Strategically applied quantum noise using a 32-qubit circuit simulation. Applied primarily to the 4th layer where feature complexity is highest.

Architecture Diagram
Visual representation of network components
Input1×28×28Conv164×14×14Conv2128×7×7Conv3/4256-512FC2048/512/10ZPE FlowZPE FlowZPE FlowZPE FlowQuantum Circuit (32 qubits)
Parameter Summary
Key hyperparameters and network configurations

Convolutional Settings

  • Filter Sizes:3×3
  • Activation:GELU
  • Pooling:Max 2×2
  • Channel Dimensions:64/128/256/512
  • Squeeze-Excite:r=16

ZPE Parameters

  • Momentum Range:0.65-0.9
  • Strength Range:0.27-0.6
  • Noise Range:0.22-0.35
  • Coupling Range:0.7-0.85
  • Max Amplitude:±0.3

Training Settings

  • Optimizer:AdamW
  • Learning Rate:0.001-0.005
  • Weight Decay:1e-4
  • Dropout:0.05-0.25
  • Label Smoothing:0.03