Quantum TensorFlow Project Description This project integrates machine learning with quantum computing to offer advanced analysis and model training. It includes TensorFlow model training, quantum circuit simulation with Cirq, and data collection through mining. The system provides real-time performance monitoring and visualization.

Table of Contents Prerequisites Installation Configuration Usage Features Contributors License Prerequisites Before you begin, make sure you have the following installed:

Python 3.7+: Download from python.org. Pip: Python package manager, usually included with Python. Bazel: Build tool for TensorFlow. Download from bazel.build. TensorFlow: Install TensorFlow via pip. Cirq: Install Cirq via pip. Scikit-learn: Install scikit-learn via pip. Matplotlib: Install matplotlib via pip. To install required Python packages, use:

bash Copier le code pip install tensorflow cirq scikit-learn matplotlib keras-tuner Installation Clone the Repository

bash Copier le code git clone https://github.com/your-repository/quantum-tensorflow-project.git cd quantum-tensorflow-project Install Dependencies

Make sure to install all the required Python packages. Use the following command:

bash Copier le code pip install -r requirements.txt Configure Bazel

Ensure Bazel is installed and correctly configured. Follow the installation instructions from Bazel's website.

Configuration TensorFlow GPU Configuration (Optional)

If using a GPU, make sure you have the correct GPU drivers and CUDA installed. Configure TensorFlow to use the GPU by setting memory growth:

python Copier le code physical_devices = tf.config.list_physical_devices('GPU') if physical_devices: try: tf.config.experimental.set_memory_growth(physical_devices[0], True) except Exception as e: print(f"Error configuring GPU: {e}") Miner Configuration

Update the following constants in the code with your miner's path and settings:

MINER_PATH: Path to the miner executable. POOL_URL: URL of the mining pool. USER: Your mining username. PASSWORD: Your mining password. python Copier le code MINER_PATH = "C:/path/to/miner" MINER_EXECUTABLE = "miner.exe" POOL_URL = "stratum+tcp://pool.example.com:port" USER = "your_username" PASSWORD = "your_password" Usage Run the Main Script

Execute the main script to start the mining process, model training, and visualization:

bash Copier le code python your_main_script.py This script will start mining, train the TensorFlow model, simulate quantum circuits, and display real-time graphs.

Monitor and Analyze

Real-time graphs will be displayed using Matplotlib, showing model performance metrics and quantum simulation results.

RMSE: Root Mean Squared Error of the model. Intercept: Intercept of the model. Intercept Adjusted: Adjusted intercept for comparison. Quantum Circuit Results: Histogram of quantum circuit simulation results. Sample vs Actual Values: Plot showing actual vs. predicted values. R^2 Score: Coefficient of Determination. Stop Mining

To stop the mining process, use a keyboard interrupt (Ctrl+C). The script will handle stopping the miner and logging the end time.

Features TensorFlow Model Training: Hyperparameter tuning and fine-tuning of models. Quantum Simulation: Creation and execution of quantum circuits with Cirq. Data Collection: Integration with a miner for collecting data. Visualization: Real-time graphs for performance metrics and quantum results. Contributors Tomy Verreault - Project Creator License This project is licensed under the Apache 2.0 License - see the LICENSE file for details.