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Raj-taware/quantum-optimization

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App README

Quantum Algorithm Optimization Environment

An OpenEnv environment for optimizing quantum algorithms using reinforcement learning. This environment simulates quantum circuits where an RL agent can modify gates to achieve better performance on real-world tasks like factoring and molecular energy calculations.

Tasks

—Easy: Optimize a 3-qubit parity circuit
—Medium: Implement Shor's algorithm for factoring 15
—Hard: Use VQE to find H2 ground state energy

Installation

bash

pip install -r requirements.txt

Usage

Running the Environment

python

from src.environment import QuantumOptimizationEnv

env = QuantumOptimizationEnv(task='parity-optimization')
obs = env.reset()
action = 0  # Add H gate
obs, reward, done, info = env.step(action)

Baseline Inference

bash

python baseline_inference.py

This runs a random policy on all tasks and outputs logs in the required format.

Deployment

Built for Hugging Face Spaces with Docker support.

Dependencies

—Qiskit: Quantum circuit simulation
—Gym: RL environment interface
—NumPy: Numerical computations

License

MIT