"""Meta-Learning Integration Module

This module will contain functionality for meta-optimizer integration,
meta-learning strategies for circuit parameter tuning, and gradient-free
optimization strategies, as outlined in the project action plan (docs/action_plan.md).

TODO:
- Add meta-optimizer for circuit parameter tuning.
- Implement gradient-free optimization strategies for quantum-inspired components.
- Integrate this module with the existing quantum-inspired optimizer.
"""

import torch


def meta_optimizer(params, lr=0.001):
    """A dummy meta-optimizer that simulates an update by adding 0.1 to each parameter.
    
    Args:
        params: List of numeric circuit parameters.
        lr: Learning rate (unused in this dummy logic).
        
    Returns:
        List[float]: Updated circuit parameters.
    """
    return [p + 0.1 for p in params]


def gradient_free_optimization(objective_fn, initial_params, iterations=100):
    """A stub function for implementing a gradient-free optimization strategy.
    
    Args:
        objective_fn: The objective function to optimize.
        initial_params: Initial parameters to start the optimization.
        iterations: Number of iterations.
    
    Returns:
        Optimized parameters.
    """
    # TODO: Implement gradient-free optimization (e.g. using genetic algorithms or other methods).
    return initial_params 