Module rl_circuit_optimization 

Reinforcement Learning-Based Quantum Circuit Optimization

This module implements advanced circuit optimization using reinforcement learning (RL). The RL agent learns optimal gate sequences, placement strategies, and circuit transformations by interacting with quantum circuits and receiving rewards based on circuit quality metrics (depth, gate count, fidelity, etc.).

Structs

CircuitState

State representation for the RL agent

OptimizationEpisode

Record of a single optimization episode

OptimizationStatistics

Statistics about optimization performance

QLearningOptimizer

Q-learning agent for circuit optimization

Enums

OptimizationAction

Actions the RL agent can take to optimize circuits