"use client"; import React from "react"; import { CircuitBoard, Braces, Layers, Zap, Atom, Network, ArrowRightLeft, ArrowLeftRight } from "lucide-react"; import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card"; import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"; import { Badge } from "@/components/ui/badge"; export default function ModelArchitecture() { return (

Model Architecture

Detailed structure of the ZPE Quantum Neural Network

Overview Layer Structure Quantum Integration
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
Input 1×28×28 Conv1 64×14×14 Conv2 128×7×7 Conv3/4 256-512 FC 2048/512/10 ZPE Flow ZPE Flow ZPE Flow ZPE Flow Quantum 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
Convolutional LayersDetailed structure of the convolutional backbone
{[ { badge: "Conv1", badgeClass: "bg-blue-100 text-blue-800 dark:bg-blue-900 dark:text-blue-300", title: "First Convolutional Block", in: "1×28×28", out: "64×14×14", params: "~1.8K", conv: "3×3, 64" }, { badge: "Conv2", badgeClass: "bg-indigo-100 text-indigo-800 dark:bg-indigo-900 dark:text-indigo-300", title: "Second Convolutional Block", in: "64×14×14", out: "128×7×7", params: "~73K", conv: "3×3, 128" }, ].map(block => (

{block.badge}{block.title}

Conv2D
{block.conv}
BatchNorm
GELU
SE Block
r=16
MaxPool
2×2

Input: {block.in} → Output: {block.out}

Parameters: {block.params}

))}

Conv3/4Deeper Convolutional Blocks

Conv2D
3×3, 256/512
BatchNorm
GELU
SE Block
r=16
MaxPool
2×2

Conv3:

Input: 128×7×7 → Output: 256×3×3

Parameters: ~295K

Conv4:

Input: 256×3×3 → Output: 512×1×1

Parameters: ~1.2M

 Additional ComponentsAuxiliary modules and network enhancements

ZPEZero-Point Energy Flow

{`def apply_zpe(self, x, zpe_idx):\n    flow_expanded = self.zpe_flows[zpe_idx].view(1, -1, 1, 1)\n    return x * flow_expanded`}

ZPE flow provides channel-wise modulation based on momentum-governed perturbations. The flow parameters evolve during training through:

  1. Previous flow state preservation via momentum
  2. Generation of perturbations based on batch statistics
  3. Optional quantum noise integration
  4. Controlled coupling across channels

SESqueeze-Excitation Block

Input Features
Global Pooling
FC (c/r)
GELU
FC (c)
Sigmoid
Input Features
Scale Features

SE blocks provide adaptive feature recalibration, dynamically emphasizing informative channels while suppressing less useful ones.

FCFully Connected Layers

{`self.fc = nn.Sequential(\n    nn.Flatten(), \n    nn.Linear(512, 2048), nn.GELU(), nn.Dropout(0.25),\n    nn.Linear(2048, 512), nn.GELU(), nn.Dropout(0.25),\n    nn.Linear(512, 10)\n)`}

The fully connected section processes flattened features through three layers with decreasing dropout rates. GELU activation provides non-linearity with smooth gradients.
Quantum IntegrationArchitecture details of quantum noise generation and application

Quantum Circuit Architecture

{`def generate_quantum_noise(self, num_channels, zpe_idx):\n    qubits_per_run = 32\n    # ... (rest of cirq code) ...\n    return torch.tensor(perturbation, device=self.device, dtype=torch.float32)`}

Implementation Notes:

  • Quantum circuit runs on a classical simulator (cirq)
  • Each qubit undergoes Hadamard gate and random rotations
  • Measurement results are transformed via tanh function
  • Multiple circuit runs handle large channel counts

Quantum-Classical Integration

{`def perturb_zpe_flow(self, data, zpe_idx):\n    # ... (classical noise or quantum noise based on zpe_idx) ...\n    # Apply momentum update\n    # ...`}

Integration Strategy:

  • Quantum noise selectively applied to 4th conv layer
  • Other layers use classical noise with correlation
  • Momentum-based update rule for both noise types
  • Perturbations bounded via tanh and clamping

Theoretical Advantages:

  • Higher-quality exploration with quantum randomness
  • Focused computational resources via layer-specific application
  • Stable state evolution with momentum
  • Feature relationship preservation via correlation
Quantum Circuit VisualizationSimplified view of the quantum circuit for a single qubit |0⟩ H Rz Rx SuperpositionPhase RotationX RotationMeasure |0⟩ → |+⟩θ ∈ [0, 2π]φ ∈ [0, π]|0⟩ or |1⟩ Performance ImpactObserved effects of quantum noise application
+1.8%
Accuracy Improvement
-12%
Overfitting Reduction
+15%
Faster Convergence
5-10×
Generation Cost

Key Findings

  • Most effective when applied to high-level feature layers
  • Benefits increase with model depth and complexity
  • Optimal coupling values are model-specific
  • Performance improvement justifies computational overhead
  • Effects are most pronounced on complex, ambiguous examples
Research StatusActive Investigation
ImplementationSimulation-Based
); }