README.md

---
license: apache-2.0
pipeline_tag: text-classification
---

# 🚀 Quantum-Neural Hybrid (Q-NH) Model Overview 🤖

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

components:
  - 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."

training_pipeline:
  - 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)

dataset:
  - Source: "jovianzm/no_robots"
  - Labels: "Classify", "Positive", "Negative"

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

custom_utilities:
  - 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.

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

future_work:
  - 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! 🚀