Documentation

Overview

PELINN-Q provides tools for quantum error mitigation with liquid neural networks. This documentation helps you use and understand the library.

📖 Class Explainers

Detailed explanations of the key classes and architectures in PELINN-Q:

Understanding LTCCell

In-depth explanation of the Liquid Time-Constant (LTC) recurrent neural network cell. Learn about:

Weights and learnable parameters (time-constant pathway, gating pathway, attractor vector)
Forward dynamics and integration with Euler steps
Regularization hooks for training

🚀 Installation

Requirements

Python 3.8+
NumPy
PyTorch
Qiskit (for quantum simulations)

Install

# Clone the repository
git clone https://github.com/BramDo/PELINN-Q.git
cd PELINN-Q

Train a Liquid Neural Network

from pelinn import LiquidNeuralNetwork
from pelinn.qem import QuantumErrorMitigator

# Initialize the network
lnn = LiquidNeuralNetwork(
    input_size=10,
    hidden_size=20,
    output_size=5
)

# Train the model
lnn.train(training_data, epochs=100)

Apply Quantum Error Mitigation

# Create a QEM mitigator
mitigator = QuantumErrorMitigator(lnn)

# Apply mitigation to quantum results
mitigated_results = mitigator.mitigate(raw_quantum_data)

📚 API Referentie

LiquidNeuralNetwork

The main class for liquid neural networks.

Parameters:

input_size (int): Number of input features
hidden_size (int): Number of hidden neurons
output_size (int): Number of output features
tau (float): Time constant for the network

Methods:

train(data, epochs): Train the network
predict(input): Predict the output for a given input

QuantumErrorMitigator

Class for quantum error mitigation.

Parameters:

model: Trained liquid neural network model

Methods:

mitigate(quantum_data): Apply error mitigation
evaluate(test_data): Evaluate mitigation performance

⚙️ Configuration

You can configure PELINN-Q through a config.ini file:

[model]
input_size = 10
hidden_size = 20
output_size = 5
learning_rate = 0.001

[training]
epochs = 100
batch_size = 32

📓 Voorbeelden

Check the notebooks/ folder for Jupyter Notebook examples and the scripts/ folder for standalone scripts.

🔧 Troubleshooting

Common issues

ImportError: Make sure all dependencies are installed

pip install -r requirements.txt

CUDA errors: Check that PyTorch is installed correctly for your system

🔗 More Information

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