https://github.com/spockoo/l58unstableminerv1.1.0.0

A robust tool for mining, but unstable.
https://github.com/spockoo/l58unstableminerv1.1.0.0

Last synced: 3 months ago
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A robust tool for mining, but unstable.

• Host: GitHub
• URL: https://github.com/spockoo/l58unstableminerv1.1.0.0
• Owner: spockoo
• License: apache-2.0
• Created: 2024-08-28T02:58:42.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-08-28T22:57:02.000Z (10 months ago)
• Last Synced: 2025-01-22T15:47:52.437Z (5 months ago)
• Language: Python
• Size: 28.3 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# L58.py Script

## Overview

The `L58.py` script is a comprehensive Python program that combines quantum computing, machine learning, and data processing. It leverages multiple advanced libraries such as TensorFlow, Cirq, and Scikit-learn to perform quantum circuit simulations, machine learning model training, and data visualization. This script is designed for users who are familiar with these technologies and require a robust tool to conduct experiments and simulations.

## Features

- **Quantum Circuit Simulation**: Utilizes Cirq to simulate quantum circuits with customizable parameters.

- **Machine Learning**: Integrates TensorFlow and Scikit-learn to create, train, and evaluate machine learning models.

- **Data Handling**: Manages data preparation, batch processing, and performance metrics tracking.

- **Visualization**: Provides real-time data visualization using Matplotlib.

- **System Interaction**: Executes external commands and manages system resources efficiently.

## Requirements

To run the `L58.py` script, you need to have the following software and libraries installed:

- Python 3.8 or higher

- TensorFlow

- Scikit-learn

- Cirq

- Matplotlib

- Keras Tuner

- NumPy

- SciPy

- Pickle (for saving/loading models and data)

- NBMiner (specific paths are configured in the script)

## Installation

1. **Clone the Repository**:

   ```bash

   git clone 

   ```

2. **Install Dependencies**:

   ```bash

   pip install tensorflow scikit-learn cirq matplotlib keras-tuner numpy scipy

   ```

3. **NBMiner Setup**:

   Ensure that the NBMiner executable is located at the specified path in the script (`C:/Users/marde/Documents/script/NBminer_Win`). You may need to adjust the path in the script if your setup differs.

## Usage

1. **Data Preparation**: 

   - Modify the `define_data` function to customize the dataset used for training the model.

   - Adjust the batch size and other parameters to suit your computational resources.

2. **Running the Script**:

   - Execute the script using Python:

     ```bash

     python L58.py

     ```

3. **Output**:

   - The script will generate real-time plots, train machine learning models, and may interact with quantum circuits depending on the parameters set.

## Configuration

- **Paths**: Ensure that all paths (e.g., `MINER_PATH`, `MODEL_FILE_PATH`) in the script are correctly set to your environment.

- **Quantum Circuit Parameters**: Adjust the `quantum_circuit_repetitions` and `initial_qubits` variables to configure the quantum circuit simulation.