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| """ | |
| This module provides quantum-inspired optimization algorithms for enhanced performance. | |
| """ | |
| try: | |
| import numpy as np | |
| except Exception: | |
| np = None | |
| import math | |
| from typing import List, Dict, Any, Callable, Optional, Tuple | |
| class QuantumInspiredOptimizer: | |
| def __init__(self): | |
| self.population_size = 50 | |
| self.num_qubits = 32 | |
| self.max_iterations = 100 | |
| self.optimization_history = [] | |
| self.best_solution = None | |
| self.best_fitness = float('-inf') | |
| def optimize(self, objective_function: Callable[[np.ndarray], float], | |
| constraints: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]: | |
| """ | |
| Perform quantum-inspired optimization. | |
| Args: | |
| objective_function: The function to optimize | |
| constraints: Optional list of constraint dictionaries | |
| Returns: | |
| Optimization results | |
| """ | |
| # Initialize quantum population | |
| population = self._initialize_quantum_population() | |
| for iteration in range(self.max_iterations): | |
| # Measure quantum states | |
| classical_solutions = self._measure_quantum_states(population) | |
| # Apply constraints if any | |
| if constraints: | |
| classical_solutions = self._apply_constraints(classical_solutions, constraints) | |
| # Evaluate fitness | |
| fitness_values = [objective_function(solution) for solution in classical_solutions] | |
| # Update best solution | |
| if np is not None: | |
| current_best_idx = int(np.argmax(fitness_values)) | |
| else: | |
| current_best_idx = int(max(range(len(fitness_values)), key=lambda i: fitness_values[i])) | |
| if fitness_values[current_best_idx] > self.best_fitness: | |
| self.best_fitness = fitness_values[current_best_idx] | |
| self.best_solution = classical_solutions[current_best_idx] | |
| # Apply quantum gates | |
| population = self._apply_quantum_gates(population, fitness_values) | |
| # Store iteration history | |
| self.optimization_history.append({ | |
| "iteration": iteration, | |
| "best_fitness": self.best_fitness, | |
| "population_diversity": self._calculate_diversity(classical_solutions) | |
| }) | |
| return { | |
| "best_solution": self.best_solution, | |
| "best_fitness": self.best_fitness, | |
| "convergence": self.optimization_history, | |
| "final_population": self._measure_quantum_states(population) | |
| } | |
| def _initialize_quantum_population(self) -> np.ndarray: | |
| """ | |
| Initialize the quantum population with superposition states. | |
| """ | |
| if np is not None: | |
| # Create population of quantum individuals | |
| population = np.zeros((self.population_size, self.num_qubits, 2), dtype=np.complex128) | |
| # Initialize in superposition state | |
| population[:, :, 0] = 1/np.sqrt(2) # amplitude for |0⟩ | |
| population[:, :, 1] = 1/np.sqrt(2) # amplitude for |1⟩ | |
| else: | |
| # Fallback simple population representation (probabilities) | |
| population = [[ [1/math.sqrt(2), 1/math.sqrt(2)] for _ in range(self.num_qubits)] for _ in range(self.population_size)] | |
| return population | |
| def _measure_quantum_states(self, population: np.ndarray) -> np.ndarray: | |
| """ | |
| Perform measurement on quantum states to get classical solutions. | |
| """ | |
| if np is not None: | |
| classical_solutions = np.zeros((self.population_size, self.num_qubits)) | |
| for i in range(self.population_size): | |
| # Calculate probabilities | |
| probabilities = np.abs(population[i, :, 1])**2 | |
| # Perform measurement | |
| classical_solutions[i] = np.random.random(self.num_qubits) < probabilities | |
| return classical_solutions | |
| else: | |
| import random | |
| classical_solutions = [] | |
| for i in range(self.population_size): | |
| probs = [abs(complex(pair[1]))**2 for pair in population[i]] | |
| classical_solutions.append([1 if random.random() < p else 0 for p in probs]) | |
| return classical_solutions | |
| def _apply_quantum_gates(self, population: np.ndarray, fitness_values: List[float]) -> np.ndarray: | |
| """ | |
| Apply quantum gates to update the quantum states. | |
| """ | |
| # Normalize fitness values | |
| if np is not None: | |
| fitness_normalized = np.array(fitness_values) | |
| fitness_normalized = (fitness_normalized - np.min(fitness_normalized)) / \ | |
| (np.max(fitness_normalized) - np.min(fitness_normalized) + 1e-10) | |
| # Quantum rotation gate | |
| theta = np.pi * fitness_normalized[:, np.newaxis] / 2 | |
| # Apply rotation | |
| for i in range(self.population_size): | |
| rotation_matrix = np.array([ | |
| [np.cos(theta[i]), -np.sin(theta[i])], | |
| [np.sin(theta[i]), np.cos(theta[i])] | |
| ]) | |
| population[i] = np.einsum('ij,kj->ki', rotation_matrix, population[i]) | |
| else: | |
| # fitness_normalized already computed by caller | |
| theta = None | |
| theta = [math.pi * f / 2 for f in fitness_normalized] | |
| # Simple perturbation fallback | |
| for i in range(self.population_size): | |
| for q in range(self.num_qubits): | |
| amp0, amp1 = population[i][q] | |
| # rotate amplitudes slightly based on theta | |
| t = theta[i] | |
| new0 = amp0*math.cos(t) - amp1*math.sin(t) | |
| new1 = amp0*math.sin(t) + amp1*math.cos(t) | |
| population[i][q] = [new0, new1] | |
| return population | |
| def _apply_constraints(self, solutions: np.ndarray, constraints: List[Dict[str, Any]]) -> np.ndarray: | |
| """ | |
| Apply constraints to classical solutions. | |
| """ | |
| constrained_solutions = solutions.copy() | |
| for constraint in constraints: | |
| constraint_type = constraint.get("type", "") | |
| if constraint_type == "bound": | |
| lower = constraint.get("lower", 0) | |
| upper = constraint.get("upper", 1) | |
| constrained_solutions = np.clip(constrained_solutions, lower, upper) | |
| elif constraint_type == "sum": | |
| target_sum = constraint.get("target", 1) | |
| current_sums = np.sum(constrained_solutions, axis=1) | |
| for i in range(len(constrained_solutions)): | |
| if current_sums[i] != 0: # Avoid division by zero | |
| constrained_solutions[i] *= target_sum / current_sums[i] | |
| return constrained_solutions | |
| def _calculate_diversity(self, population: np.ndarray) -> float: | |
| """ | |
| Calculate diversity of the population. | |
| """ | |
| return np.mean([ | |
| np.mean(np.abs(p1 - p2)) | |
| for i, p1 in enumerate(population) | |
| for p2 in population[i+1:] | |
| ]) | |
| def get_optimization_history(self) -> List[Dict[str, Any]]: | |
| """ | |
| Get the history of optimization progress. | |
| """ | |
| return self.optimization_history | |
| def reset(self): | |
| """ | |
| Reset the optimizer state. | |
| """ | |
| self.optimization_history = [] | |
| self.best_solution = None | |
| self.best_fitness = float('-inf') | |
| def get_quantum_state(self) -> Dict[str, Any]: | |
| """ | |
| Get the current quantum state information. | |
| """ | |
| if not self.optimization_history: | |
| return {"status": "not_initialized"} | |
| return { | |
| "iterations_completed": len(self.optimization_history), | |
| "current_best_fitness": self.best_fitness, | |
| "convergence_trend": [h["best_fitness"] for h in self.optimization_history], | |
| "diversity_trend": [h["population_diversity"] for h in self.optimization_history] | |
| } |