The Quantum-Inspired Recursive Neural Network (QIRNN) is a novel architecture inspired by principles from quantum computing and neural networks. This README provides an overview of the QIRNN implementation and its key components.

The QuantumAdaptiveComputationTime module implements an adaptive computation time mechanism to dynamically adjust the number of processing steps based on the input data. It utilizes an RNN-based mechanism to determine when to halt processing for each input sequence.

The QIRNN_Adaptive module represents the core architecture of the Quantum-Inspired Recursive Neural Network with Adaptive Computation Time. It consists of several key components:

• QuantumEmbedding: Embedding layer for input sequences.
• QuantumLayerEfficient: Efficient quantum layer for processing quantum states.
• QuantumSelfAttention: Self-attention mechanism inspired by quantum principles.
• QuantumDynamicRouting: Dynamic routing mechanism for information flow.
• QuantumAdaptiveComputationTime: Adaptive computation time mechanism described above.
• QuantumLayerNorm: Layer normalization for quantum layers.
• QuantumGate: Gate mechanism for controlling quantum state transformations.
• RecursiveCollapse: Recursive collapse operation for output aggregation.
• Linear: Linear transformation for final output projection.

# Example usage of the adaptive model
model_adaptive = QIRNN_Adaptive(vocab_size, embedding_dim, hidden_dim, num_layers, output_dim, heads, routing_iterations=3, stabilization_factor=0.1, max_steps=10)
output_adaptive = model_adaptive(x)
print(output_adaptive)

To use the QIRNN implementation, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies (PyTorch, NumPy, etc.).
3. Import the necessary modules into your Python environment.
4. Use the provided code examples to integrate the QIRNN into your project.

This project is licensed under the MIT License - see the LICENSE file for details.

• This implementation draws inspiration from various research papers and articles in the fields of quantum computing and neural networks.
• Special thanks to the contributors and maintainers of the PyTorch library for providing powerful tools for deep learning research.

For any questions or issues regarding the QIRNN implementation, please open an issue on the GitHub repository.