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πŸš€ Quantum-Neural Hybrid (Q-NH) Model Overview πŸ€–

Embark on a cosmic computational journey with the Quantum-Neural Hybrid (Q-NH) model – a symphony of quantum magic and neural network prowess. πŸš€πŸ€–πŸ“š This futuristic oracle decodes language intricacies, processes sentiments, and offers a high-tech experience inspired by BERT but with a unique twist, merging quantum tricks and neural network wizardry for extraordinary text analysis and understanding. 🧠🌌

model_description: > A cutting-edge fusion of quantum computing 🌌 and neural networks 🧠 for advanced language understanding and sentiment analysis.


  • quantum_module: num_qubits: 5 depth: 3 num_shots: 1024 description: "Parameterized quantum circuit with single and two-qubit errors, tailored for language processing tasks."

  • neural_network: architecture: - Linear: 2048 neurons - ReLU activation - LSTM: 2048 neurons, 2 layers, 20% dropout - Multihead Attention: 64 heads, key and value dimensions of 2048 - Linear: Output layer with 3 classes, followed by Sigmoid activation optimizer: Adam with learning rate 0.001 loss_function: CrossEntropyLoss description: "Neural network integrating LSTM, Multihead Attention, and classical layers for comprehensive language analysis."


  • QNALS-Transformer Integration:
    • Quantum module pre-processes input for quantum features.
    • Transformer model (BERT) processes tokenized input sequences.
    • Outputs from both components concatenated and passed through a classifier.
  • Hyperparameters:
    • Batch size: 32
    • Learning rate: 0.0001 (AdamW optimizer)
    • Training epochs: 10 (with checkpointing and learning rate scheduling)


  • Source: "jovianzm/no_robots"
  • Labels: "Classify", "Positive", "Negative"


  • PyTorch: Deep learning framework
  • Qiskit: Quantum computing framework
  • Transformers: State-of-the-art natural language processing models
  • Matplotlib: Visualization of training progress


  • NoiseModel: Custom quantum noise model with amplitude damping and depolarizing errors.
  • QNALS: Quantum-Neural Adaptive Learning System, integrating quantum circuit and neural network.
  • FinalModel: Custom PyTorch model combining QNALS and BERT for end-to-end language analysis.


  • Epochs: 10
  • Visualization: Training loss and accuracy plotted for each epoch.


  • Extended Training:
    • Additional epochs for the QNALS component.
  • Model Saving:
    • Checkpoints and weights saved for both QNALS and the final integrated model.
    • Entire model architecture and optimizer state saved for future use.

🌐 Explore the Quantum Realm of Language Understanding! πŸš€

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