量子计算对软件测试的影响初探_千锋教育

# 传统测试：确定性断言
def test_add():
    result = add(1, 2)
    assert result == 3  # 通过

# 量子测试：概率性断言
def test_quantum_factor():
    results = []
    for _ in range(1000):
        result = quantum_factor(15)  # 可能返回3或5
        results.append(result)
    # 检查3或5出现的概率是否超过80%
    prob_3 = results.count(3) / 1000
    prob_5 = results.count(5) / 1000
    assert max(prob_3, prob_5) > 0.8  # 通过

import numpy as np

def edge_noise_simulator(circuit, noise_level=0.05):
    """模拟边缘环境噪声"""
    # 模拟量子门操作时引入随机误差
    for gate in circuit.gates:
        if np.random.random() < noise_level:
            gate.angle += np.random.normal(0, 0.1)  # 角度偏差
    # 模拟测量时引入比特翻转
    for qubit in range(circuit.num_qubits):
        if np.random.random() < noise_level * 0.5:
            circuit.flip_measurement(qubit)  # 反转结果
    return circuit.run()

def test_quantum_circuit_in_edge():
    for test_case in test_cases:
        # 运行10次，每次使用不同噪声
        results = [edge_noise_simulator(test_case.circuit, noise_level=0.1) for _ in range(10)]
        # 检查结果稳定性：至少7次结果一致
        assert max(results.count(r) for r in set(results)) >= 7

import requests

class QuantumMock:
    def __init__(self, use_real_qrng=False):
        self.use_real_qrng = use_real_qrng
        self.log = []
    
    def generate_key(self, length):
        if self.use_real_qrng:
            # 调用真实量子随机数API
            response = requests.get(f"https://qrng.anu.edu.au/API/jsonI.php?length={length}")
            key = response.json()['data']
        else:
            # 使用伪随机数，但记录种子
            key = [random.randint(0, 255) for _ in range(length)]
        self.log.append(key)  # 记录用于回放
        return key
    
    def replay(self, test_id):
        """回放特定测试的随机数"""
        return self.log[test_id]

def test_qkd_system():
    mock = QuantumMock(use_real_qrng=False)
    for i in range(100):
        key = mock.generate_key(256)
        assert qkd_system.process_key(key) is not None
    # 验证随机性：检查自相关性
    assert check_randomness(mock.log) > 0.95

from qiskit import QuantumCircuit, Aer, execute
from qiskit.quantum_info import state_fidelity

def test_quantum_gate():
    # 构建目标电路：生成贝尔态
    qc = QuantumCircuit(2, 2)
    qc.h(0)
    qc.cx(0, 1)
    qc.measure_all()
    
    # 运行并获取结果统计
    backend = Aer.get_backend('qasm_simulator')
    counts = execute(qc, backend, shots=10000).result().get_counts()
    
    # 计算理论分布：00和11各占50%
    expected = {'00': 5000, '11': 5000}
    
    # 使用卡方检验验证分布
    from scipy.stats import chisquare
    observed = [counts.get('00', 0), counts.get('11', 0)]
    expected_counts = [5000, 5000]
    stat, p_value = chisquare(observed, f_exp=expected_counts)
    
    # 如果p值>0.05，认为分布与理论一致
    assert p_value > 0.05