#!/usr/bin/env python3
"""
Adaptive Control Prototype for Quantum Simulation Optimization

This script implements a simple adaptive control loop that monitors a simulated metric
(representing, for example, error rates or circuit performance) and adjusts simulation
parameters when the metric exceeds a set threshold.

This prototype is intended as low-hanging fruit for achieving significant improvements
with minimal code changes. It serves as a foundation for a more advanced quantum adaptive
optimizer module in our project.

Note: This code is experimental and contains extensive inline documentation for clarity.
"""

import time
import random
import logging

# Setup logging for real-time monitoring
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Initial dummy simulation parameters
simulation_parameters = {
    "error_rate": 0.05,    # initial error rate; lower is better
    "circuit_depth": 10,   # initial circuit depth; represents complexity
}


def get_simulation_metric():
    """
    Simulates the collection of a simulation metric.
    In an actual implementation, this function would interface with qio_module.py
    or similar modules to retrieve real performance data (e.g., error rate, latency).

    Returns:
        float: A simulated metric value.
    """
    # For demonstration, we simulate a metric with a random value in [0, 0.1]
    metric = random.uniform(0.0, 0.1)
    logger.debug('Simulation metric collected: %.4f', metric)
    return metric


def adjust_parameters(metric, threshold=0.07):
    """
    Adjusts simulation parameters if the metric exceeds a specified threshold.

    Args:
        metric (float): The current simulation metric.
        threshold (float, optional): The threshold above which adjustments are made.

    Returns:
        bool: True if adjustments were made, False otherwise.

    This function demonstrates a simple control algorithm:
    - If the metric (e.g., error rate) is above the threshold, reduce the error_rate
      and potentially adjust the circuit_depth.
    """
    adjusted = False
    if metric > threshold:
        # Adjust error_rate by reducing it by 5%, ensuring it doesn't go below 0.01
        new_error_rate = max(simulation_parameters["error_rate"] * 0.95, 0.01)
        logger.debug('Metric %.4f exceeds threshold %.4f. Adjusting error_rate: %.4f -> %.4f',
                     metric, threshold, simulation_parameters["error_rate"], new_error_rate)
        simulation_parameters["error_rate"] = new_error_rate
        
        # Adjust circuit depth heuristically by reducing by 2%
        new_circuit_depth = max(int(simulation_parameters["circuit_depth"] * 0.98), 1)
        simulation_parameters["circuit_depth"] = new_circuit_depth
        adjusted = True
    else:
        logger.debug('Metric %.4f is below threshold %.4f. No adjustments made.', metric, threshold)
    return adjusted


def adaptive_control_loop(iterations=10, delay=1.0):
    """
    Runs the adaptive control loop for a fixed number of iterations with a delay between iterations.

    Args:
        iterations (int): Number of iterations to run the control loop.
        delay (float): Delay in seconds between each iteration.
    """
    logger.info('Starting adaptive control loop.')
    adjustments_count = 0
    iteration_times = []
    for i in range(iterations):
        iteration_start = time.time()
        logger.info('Iteration %d', i+1)
        metric = get_simulation_metric()
        if adjust_parameters(metric):
            logger.info('Parameters adjusted: %s', simulation_parameters)
            adjustments_count += 1
        else:
            logger.info('No adjustment performed. Current parameters: %s', simulation_parameters)
        iteration_end = time.time()
        iteration_duration = iteration_end - iteration_start
        iteration_times.append(iteration_duration)
        logger.info('Iteration %d completed in %.4f seconds', i+1, iteration_duration)
        time.sleep(delay)
    avg_iteration_time = sum(iteration_times) / len(iteration_times) if iteration_times else 0
    logger.info('Adaptive control loop completed.')
    logger.info('Total iterations: %d', iterations)
    logger.info('Total adjustments made: %d', adjustments_count)
    logger.info('Average iteration time: %.4f seconds', avg_iteration_time)


if __name__ == '__main__':
    adaptive_control_loop() 