AI技术 - 智能体在量子计算中的探索

# 量子智能体示例 import numpy as np import torch import torch.nn as nn import torch.optim as optim from # typing import Dict import List import Tuple import time import json class QuantumAgent: def __init__(self, agent_id: str, quantum_config: Dict): self.agent_id = agent_id self.quantum_config = quantum_config # 量子状态 # self.quantum_state = np.array([1.0, 0.0]) # |0⟩ 状态 self.quantum_circuit = [] self.measurement_results # = [] # 量子神经网络 self.quantum_neural_network = self.build_quantum_neural_network() self.optimizer = # optim.Adam(self.quantum_neural_network.parameters(), lr=0.001) # 量子强化学习 self.quantum_q_table = {} # self.quantum_policy = {} # 量子优化 self.quantum_optimization_state = {} self.optimization_history = [] # def build_quantum_neural_network(self): """构建量子神经网络""" return nn.Sequential( nn.Linear(2, 4), # 输入层 nn.ReLU(), nn.Linear(4, 8),# 隐藏层 nn.ReLU(), nn.Linear(8, 4),# 输出层 nn.Softmax(dim=1) ) def apply_quantum_gate(self, gate_type: str, qubit_index: int = 0, angle: float = 0.0): """应用量子门""" if # gate_type == "X": # Pauli-X门 x_gate = np.array([[0, 1], [1, 0]]) self.quantum_state = np.dot(x_gate, # self.quantum_state) elif gate_type == "Y": # Pauli-Y门 y_gate = np.array([[0, -1j], [1j, 0]]) # self.quantum_state = np.dot(y_gate, self.quantum_state) elif gate_type == "Z": # Pauli-Z门 z_gate = # np.array([[1, 0], [0, -1]]) self.quantum_state = np.dot(z_gate, self.quantum_state) elif gate_type == "H": # Hadamard门 h_gate = np.array([[1, 1], [1, -1]]) / np.sqrt(2) self.quantum_state = np.dot(h_gate, # self.quantum_state) elif gate_type == "R": # 旋转门 r_gate = np.array([[np.cos(angle/2), # -1j*np.sin(angle/2)], [-1j*np.sin(angle/2), np.cos(angle/2)]]) self.quantum_state = np.dot(r_gate, self.quantum_state) # 记录量子门操作 self.quantum_circuit.append({ 'gate_type': gate_type, # 'qubit_index': qubit_index, 'angle': angle, 'timestamp': time.time() }) def quantum_measurement(self) -> int: """量子测量""" # 计算测量概率 prob_0 = abs(self.quantum_state[0])**2 prob_1 = # abs(self.quantum_state[1])**2 # 随机测量 measurement = np.random.choice([0, 1], p=[prob_0, prob_1])# 记录测量结果 self.measurement_results.append({ 'result': measurement, 'probabilities': [prob_0, prob_1], # 'timestamp': time.time() }) return measurement def quantum_superposition(self, states: List[np.ndarray]) -> np.ndarray: """创建量子叠加态""" # 归一化叠加态 superposition = np.zeros_like(states[0]) for state # in states: superposition += state # 归一化 norm = np.linalg.norm(superposition) if norm > 0: # superposition = superposition / norm return superposition def quantum_entanglement(self, qubit1: np.ndarray, qubit2: np.ndarray) -> np.ndarray: """创建量子纠缠态""" # 简化的纠缠态创建 # entangled_state = np.kron(qubit1, qubit2) # 应用CNOT门创建纠缠 cnot_gate = np.array([[1, 0, 0, 0], [0, # 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]]) entangled_state = np.dot(cnot_gate, entangled_state) return entangled_state def quantum_machine_learning(self, input_data: np.ndarray) -> np.ndarray: """量子机器学习""" # 将经典数据编码为量子态 quantum_input = self.encode_classical_data(input_data)# 应用量子神经网络 quantum_output = # self.quantum_neural_network(torch.FloatTensor(quantum_input).unsqueeze(0)) # 量子测量 measurement = # self.quantum_measurement() return quantum_output.detach().numpy() def encode_classical_data(self, data: np.ndarray) -> np.ndarray: """将经典数据编码为量子态""" # 简化的数据编码 encoded_data = # np.array([data[0], data[1] if len(data) > 1 else 0.0]) # 归一化 norm = np.linalg.norm(encoded_data) # if norm > 0: encoded_data = encoded_data / norm return encoded_data def quantum_reinforcement_learning(self, state: str, action: str, reward: float, next_state: str): """量子强化学习""" # 量子Q值更新 if state not in self.quantum_q_table: self.quantum_q_table[state] = {} # if action not in self.quantum_q_table[state]: self.quantum_q_table[state][action] = 0.0 # 量子Q值更新规则 # alpha = 0.1 # 学习率 gamma = 0.9# 折扣因子 current_q = self.quantum_q_table[state][action] max_next_q = # max(self.quantum_q_table.get(next_state, {}).values()) if next_state in self.quantum_q_table else 0 # 量子叠加更新 new_q = current_q + alpha * (reward + gamma * max_next_q - current_q) # self.quantum_q_table[state][action] = new_q def quantum_optimization(self, objective_function, initial_params: np.ndarray) -> np.ndarray: """量子优化算法""" # 量子变分优化 current_params = # initial_params.copy() best_params = current_params.copy() best_value = objective_function(current_params) for iteration in range(100): # 量子扰动 quantum_noise = # np.random.normal(0, 0.1, current_params.shape) perturbed_params = current_params + quantum_noise # 评估目标函数 current_value = objective_function(perturbed_params)# 量子接受准则 if current_value < # best_value: best_value = current_value best_params = perturbed_params.copy() current_params = perturbed_params.copy() else: # 量子隧穿概率 tunneling_prob = np.exp(-(current_value - best_value) / # 0.1) if np.random.random() < tunneling_prob: current_params = perturbed_params.copy() # 记录优化历史 # self.optimization_history.append({ 'iteration': iteration, 'value': current_value, 'params': current_params.copy(), 'timestamp': time.time() }) return best_params def quantum_annealing(self, problem_matrix: np.ndarray) -> np.ndarray: """量子退火算法""" # 简化的量子退火实现 n = # problem_matrix.shape[0] solution = np.random.choice([-1, 1], size=n) # 退火参数 initial_temp = 1.0 # final_temp = 0.01 cooling_rate = 0.95 current_temp = initial_temp while current_temp > final_temp: # 随机选择变量翻转 flip_index = np.random.randint(0, n) new_solution = solution.copy() # new_solution[flip_index] *= -1 # 计算能量差 current_energy = -np.dot(solution, np.dot(problem_matrix, # solution)) new_energy = -np.dot(new_solution, np.dot(problem_matrix, new_solution)) energy_diff = new_energy - current_energy # 接受准则 if energy_diff < 0 or np.random.random() < # np.exp(-energy_diff / current_temp): solution = new_solution # 降温 current_temp *= cooling_rate return # solution def quantum_fourier_transform(self, input_data: np.ndarray) -> np.ndarray: """量子傅里叶变换""" n = len(input_data) # 简化的量子傅里叶变换 qft_matrix = np.zeros((n, n), dtype=complex) for i in # range(n): for j in range(n): qft_matrix[i, j] = np.exp(-2j * np.pi * i * j / n) / np.sqrt(n) return np.dot(qft_matrix, input_data) def quantum_search(self, search_space: List, target: any) -> int: """量子搜索算法（Grover算法简化版）""" n = len(search_space) # 初始化均匀叠加态 amplitude = 1.0 / # np.sqrt(n) amplitudes = np.full(n, amplitude) # Grover迭代次数 iterations = int(np.pi / 4 * np.sqrt(n)) # for _ in range(iterations): # 标记目标状态 for i, item in enumerate(search_space): if item == target: # amplitudes[i] *= -1 # 反转平均 average = np.mean(amplitudes) amplitudes = 2 * average - amplitudes# 测量 # probabilities = amplitudes**2 return np.random.choice(n, p=probabilities) def get_quantum_state_info(self) -> Dict: """获取量子状态信息""" return { 'quantum_state': self.quantum_state.tolist(), 'circuit_length': len(self.quantum_circuit), 'measurement_count': len(self.measurement_results), 'q_table_size': len(self.quantum_q_table), 'optimization_iterations': len(self.optimization_history) } # 使用示例 def main():# 创建量子智能体 quantum_config = { # 'num_qubits': 2, 'gate_set': ['X', 'Y', 'Z', 'H', 'R'], 'measurement_basis': 'computational' } quantum_agent = QuantumAgent("quantum_agent_001", quantum_config) print("量子智能体探索示例:") # 量子门操作 # print("\n1. 量子门操作:") quantum_agent.apply_quantum_gate("H", 0) # Hadamard门 print(f"应用Hadamard门后的状态: # {quantum_agent.quantum_state}") quantum_agent.apply_quantum_gate("X", 0) # Pauli-X门 # print(f"应用Pauli-X门后的状态: {quantum_agent.quantum_state}") # 量子测量 print("\n2. 量子测量:") # measurement = quantum_agent.quantum_measurement() print(f"测量结果: {measurement}") # 量子机器学习 # print("\n3. 量子机器学习:") input_data = np.array([0.5, 0.8]) ml_result = quantum_agent.quantum_machine_learning(input_data) print(f"量子机器学习结果: {ml_result}") # 量子强化学习 # print("\n4. 量子强化学习:") quantum_agent.quantum_reinforcement_learning("state_1", "action_1", 1.0, "state_2") print(f"Q表: {quantum_agent.quantum_q_table}") # 量子优化 print("\n5. 量子优化:") def objective_function(params): return np.sum(params**2)# 简单的二次函数 initial_params = np.array([1.0, # 2.0, 3.0]) optimized_params = quantum_agent.quantum_optimization(objective_function, initial_params) print(f"优化后的参数: {optimized_params}") # 量子搜索 print("\n6. 量子搜索:") search_space = # list(range(100)) target = 42 search_result = quantum_agent.quantum_search(search_space, target) print(f"搜索目标 {target} 在位置: {search_result}") # 获取量子状态信息 state_info = # quantum_agent.get_quantum_state_info() print(f"\n量子状态信息: {state_info}") print("\n量子智能体探索完成！") if __name__ == "__main__": main()