# 2. 创建启动U盘
sudo dd bs=4M if=rhel-9.3-x86_64-dvd.iso of=/dev/sdb status=progress conv=fsync

# 3. 安装RHEL 9.3
# 插入U盘，启动计算机
# 按照安装向导进行操作
# 选择语言、键盘布局、网络配置等
# 设置用户名和密码
# 选择安装类型，推荐服务器安装

# 4. 配置网络
sudo nmcli con mod eth0 ipv4.addresses 192.168.1.100/24
sudo nmcli con mod eth0 ipv4.gateway 192.168.1.1
sudo nmcli con mod eth0 ipv4.dns “8.8.8.8 8.8.4.4”
sudo nmcli con mod eth0 ipv4.method manual
sudo nmcli con up eth0

# 5. 更新系统
sudo dnf update -y

# 2. 安装量子开发框架
# 安装Qiskit
pip3 install qiskit qiskit-terra qiskit-aer qiskit-ibmq-provider

# 安装Cirq
pip3 install cirq

# 安装PennyLane
pip3 install pennylane

# 3. 安装量子模拟软件
pip3 install qutip projectq

# 4. 安装经典计算软件
pip3 install numpy scipy matplotlib jupyter tensorflow torch

# 5. 验证安装
python3 -c “import qiskit; print(qiskit.__version__)”
python3 -c “import cirq; print(cirq.__version__)”
python3 -c “import pennylane; print(pennylane.__version__)”

# 2. 配置系统限制
sudo vim /etc/security/limits.conf
# 添加以下内容
* soft nofile 65536
* hard nofile 65536

# 3. 配置系统日志
sudo vim /etc/systemd/journald.conf
# 修改以下内容
SystemMaxUse=100M

# 4. 启用防火墙
sudo firewall-cmd –permanent –add-port=22/tcp
sudo firewall-cmd –permanent –add-port=8888/tcp
sudo firewall-cmd –reload

# 5. 配置Jupyter Notebook
sudo mkdir -p /opt/jupyter
jupyter notebook –generate-config

# 配置Jupyter Notebook密码
jupyter notebook password

# 配置Qiskit
python3 -c “from qiskit import IBMQ; IBMQ.save_account(‘YOUR_API_KEY’)”

# 2. 配置本地量子模拟器
sudo mkdir -p /opt/quantum/simulator

# 3. 配置量子计算服务
sudo cat > /etc/systemd/system/quantum-service.service << 'EOF' [Unit] Description=Quantum Computing Service After=network.target [Service] Type=simple User=quantum WorkingDirectory=/opt/quantum ExecStart=/usr/bin/python3 /opt/quantum/service.py Restart=on-failure [Install] WantedBy=multi-user.target EOF # 4. 启动量子计算服务 sudo systemctl daemon-reload sudo systemctl start quantum-service sudo systemctl enable quantum-service

# 2. 创建量子机器学习脚本
cat > quantum_ml.py << 'EOF' import pennylane as qml from pennylane import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # 加载数据 data = load_iris() X = data.data y = data.target # 数据预处理 scaler = StandardScaler() X = scaler.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 定义量子电路 dev = qml.device('default.qubit', wires=4) @qml.qnode(dev) def quantum_circuit(x, weights): # 数据编码 for i in range(4): qml.RY(x[i], wires=i) # 量子神经网络 for i in range(4): qml.Rot(weights[i, 0], weights[i, 1], weights[i, 2], wires=i) # 测量 return [qml.expval(qml.PauliZ(wires=i)) for i in range(3)] # 定义成本函数 def cost(weights, X, y): predictions = [] for x in X: pred = quantum_circuit(x, weights) predictions.append(np.argmax(pred)) return np.mean(predictions != y) # 初始化权重 np.random.seed(42) weights = np.random.random((4, 3)) # 优化 opt = qml.GradientDescentOptimizer(stepsize=0.1) for i in range(100): weights, cost_val = opt.step_and_cost(cost, weights, X_train, y_train) if (i + 1) % 10 == 0: print(f"Step {i+1}, Cost: {cost_val:.4f}") # 测试 predictions = [] for x in X_test: pred = quantum_circuit(x, weights) predictions.append(np.argmax(pred)) accuracy = np.mean(predictions == y_test) print(f"Test Accuracy: {accuracy:.4f}") EOF # 3. 运行量子机器学习应用 python3 quantum_ml.py

# 2. 创建量子密钥分发脚本
cat > quantum_key_distribution.py << 'EOF' import qiskit from qiskit import QuantumCircuit, Aer, transpile import numpy as np # 量子密钥分发协议（BB84） def generate_bits(n): """生成随机比特""" return np.random.randint(0, 2, n) def generate_bases(n): """生成随机基""" return np.random.randint(0, 2, n) # 0: rectilinear, 1: diagonal def prepare_qubits(bits, bases): """根据比特和基准备量子比特""" circuits = [] for bit, base in zip(bits, bases): qc = QuantumCircuit(1, 1) if bit == 1: qc.x(0) if base == 1: qc.h(0) qc.measure(0, 0) circuits.append(qc) return circuits def measure_qubits(circuits, bases): """根据基测量量子比特""" backend = Aer.get_backend('qasm_simulator') results = [] for qc, base in zip(circuits, bases): if base == 1: qc.h(0) transpiled_qc = transpile(qc, backend) job = backend.run(transpiled_qc, shots=1) counts = job.result().get_counts() result = int(list(counts.keys())[0]) results.append(result) return results def sift_keys(alice_bits, alice_bases, bob_bases, bob_results): """筛选密钥""" key = [] for a_bit, a_base, b_base, b_result in zip(alice_bits, alice_bases, bob_bases, bob_results): if a_base == b_base: key.append(a_bit) return key def main(): # 生成密钥长度 n = 100 # Alice生成比特和基 alice_bits = generate_bits(n) alice_bases = generate_bases(n) # Alice准备量子比特 circuits = prepare_qubits(alice_bits, alice_bases) # Bob生成基并测量 bob_bases = generate_bases(n) bob_results = measure_qubits(circuits, bob_bases) # 筛选密钥 alice_key = sift_keys(alice_bits, alice_bases, bob_bases, bob_results) bob_key = sift_keys(bob_results, bob_bases, alice_bases, alice_bits) # 验证密钥 print(f"Alice's key: {alice_key[:10]}...") print(f"Bob's key: {bob_key[:10]}...") print(f"Key length: {len(alice_key)}") print(f"Keys match: {alice_key == bob_key}") if __name__ == "__main__": main() EOF # 3. 运行量子密钥分发应用 python3 quantum_key_distribution.py