Usage

Basic Usage

mohituQ provides tools for modeling and optimizing ocean plastic cleanup operations using quantum and quantum-inspired algorithms.

Running an Optimization

You can run a sample optimization using the provided configuration:

python src/dqi_max_xorsat_implementation.py

python src/implementingQAOA_N_by_N.py

The configuration file specifies parameters such as:

  • Ocean current models

  • Plastic distribution data

  • Optimization objectives

  • Algorithm selection (DQI, QAOA, etc.)

  • Hardware backend (simulator or quantum device)

Visualizing Results

After running an optimization, you can visualize the results:

python visualize.py --input results/sample_output.json

This will generate visualizations showing:

  • Optimal placement of collection systems

  • Cleanup route optimization

  • Efficiency metrics

  • Comparison with baseline strategies

Example Scripts

The examples/ directory contains sample scripts demonstrating various use cases:

  • examples/basic_optimization.py: Simple optimization of collection points

  • examples/multi_objective.py: Balancing multiple objectives (collection efficiency, cost, etc.)

  • examples/seasonal_variation.py: Optimizing for seasonal changes in ocean currents

  • examples/real_data_integration.py: Using real-world plastic distribution data

API Reference

For detailed API documentation, see the API section in the sidebar.