# Install Qiskit via pip
pip install qiskit

# qiskit_config.yaml
[default]
backend = 'ibmq_qasm_simulator'
api_token = '<YOUR_IBM_Q_API_TOKEN>'

from qiskit import QuantumCircuit, execute, Aer
from qiskit.visualization import plot_histogram

# Create a simple quantum circuit with 2 qubits
qc = QuantumCircuit(2)
qc.h(0)  # Hadamard gate on qubit 0
qc.cx(0, 1)  # CNOT gate between qubits 0 and 1

# Execute on simulator
simulator = Aer.get_backend('qasm_simulator')
job = execute(qc, simulator, shots=1024)
result = job.result()
counts = result.get_counts(qc)
print(counts)

# Command to visualize the circuit in Jupyter Notebook
qc.draw('mpl')

# Save configuration for Amazon Braket (AWS)
[default]
aws_access_key_id = <YOUR_AWS_ACCESS_KEY_ID>
aws_secret_access_key = <YOUR_AWS_SECRET_ACCESS_KEY>
region = us-west-2

import numpy as np
from qiskit import Aer, execute, QuantumCircuit
from qiskit.circuit.library import TwoLocal
from qiskit.algorithms import VQE
from qiskit.opflow import Z, I, X, StateFn, PauliSumOp
from qiskit.utils import QuantumInstance
from qiskit.algorithms.optimizers import COBYLA

# Define a simple Hamiltonian for optimization
hamiltonian = PauliSumOp.from_list([('ZI', 1), ('IZ', 1), ('ZZ', 1)])

# Create a variational form (ansatz)
ansatz = TwoLocal(2, ['ry', 'rz'], 'cz', reps=2)

# Set up VQE algorithm
optimizer = COBYLA(maxiter=100)
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend)
vqe = VQE(ansatz, optimizer=optimizer, quantum_instance=quantum_instance)

# Run VQE to minimize the expectation value of the Hamiltonian
result = vqe.compute_minimum_eigenvalue(hamiltonian)
print("Minimum eigenvalue:", result.eigenvalue.real)

# Classical pre-processing: normalizing input data
def normalize(data):
    min_val, max_val = np.min(data), np.max(data)
    return (data - min_val) / (max_val - min_val)

samples = np.array([5, 10, 15, 20])
normalized_samples = normalize(samples)
print(normalized_samples)

# Hybrid workflow: combining classical and quantum steps
def hybrid_optimizer(classical_data):
    norm_data = normalize(classical_data)
    # Use norm_data in quantum ansatz parameterization
    # ... (see previous VQE example)
    return result.eigenvalue.real

hybrid_result = hybrid_optimizer(samples)
print("Hybrid optimizer result:", hybrid_result)

# Error handling: check backend availability
try:
    backend = Aer.get_backend('statevector_simulator')
except Exception as e:
    print(f"Error: {e}. Please ensure Qiskit and Aer are installed correctly.")

# Test quantum circuit with mock data
test_data = np.random.randint(0, 20, 4)
print("Test input:", test_data)
print("Hybrid optimizer test result:", hybrid_optimizer(test_data))

# Install required packages
pip install qiskit-machine-learning scikit-learn

from qiskit_machine_learning.kernels import QuantumKernel
from qiskit import QuantumCircuit, Aer
from sklearn.svm import SVC
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Generate synthetic dataset
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a quantum circuit for kernel
def feature_map(x):
    qc = QuantumCircuit(2)
    qc.h(0)
    qc.ry(x[0], 0)
    qc.ry(x[1], 1)
    qc.cx(0, 1)
    return qc

# Define quantum kernel
quantum_kernel = QuantumKernel(feature_map=feature_map, quantum_instance=Aer.get_backend('statevector_simulator'))

# Integrate quantum kernel with scikit-learn SVM
svc = SVC(kernel=quantum_kernel.evaluate)
svc.fit(X_train, y_train)
y_pred = svc.predict(X_test)
print("Quantum SVM Accuracy:", accuracy_score(y_test, y_pred))

# Compare with classical classifier
from sklearn.svm import LinearSVC
lin_svc = LinearSVC(max_iter=10000)
lin_svc.fit(X_train, y_train)
y_pred_classical = lin_svc.predict(X_test)
print("Classical SVM Accuracy:", accuracy_score(y_test, y_pred_classical))

# Visualize decision boundaries (inline in Jupyter)
import matplotlib.pyplot as plt
import numpy as np
xx, yy = np.meshgrid(np.linspace(X[:,0].min(), X[:,0].max(), 100), np.linspace(X[:,1].min(), X[:,1].max(), 100))
Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.4)
plt.scatter(X[:,0], X[:,1], c=y)
plt.title("Quantum SVM Decision Boundary")
plt.show()

# Install Amazon Braket SDK
pip install amazon-braket-sdk

from braket.aws import AwsDevice, AwsQuantumTask
from braket.circuits import Circuit
import time

# Choose a quantum device (simulator or real hardware)
device = AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1")

# Build a simple quantum circuit
circuit = Circuit().h(0).cnot(0, 1)

# Submit quantum task
task = device.run(circuit, shots=1000)

# Wait for completion and retrieve results
while not task.state() == 'COMPLETED':
    print("Waiting for job to complete...")
    time.sleep(5)
result = task.result()
print("Measurement counts:", result.measurement_counts)

# .braket/config
[default]
aws_access_key_id = <YOUR_AWS_ACCESS_KEY_ID>
aws_secret_access_key = <YOUR_AWS_SECRET_ACCESS_KEY>
region = us-west-2

# Integrate quantum workflow in CI/CD pipeline (GitHub Actions example)
name: Quantum CI Pipeline
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.10'
      - name: Install dependencies
        run: |
          pip install qiskit amazon-braket-sdk
      - name: Run tests
        run: |
          python -m unittest discover tests/

# Error handling: monitor quantum job states
def check_task_status(task):
    try:
        state = task.state()
        if state == 'FAILED':
            print("Quantum job failed. Check logs for details.")
        elif state == 'COMPLETED':
            print("Quantum job completed successfully.")
        else:
            print(f"Quantum job state: {state}")
    except Exception as e:
        print(f"Error monitoring quantum job: {e}")

# Simulate noise model
from qiskit.providers.aer.noise import NoiseModel, depolarizing_error
from qiskit import Aer, execute, QuantumCircuit

noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(depolarizing_error(0.01, 1), ['u1', 'u2', 'u3'])

qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)

simulator = Aer.get_backend('qasm_simulator')
job = execute(qc, simulator, noise_model=noise_model, shots=1024)
result = job.result()
print(result.get_counts())

# Optimize quantum circuit for hardware
from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import Optimize1qGates

optimized_qc = qc.copy()
pass_manager = PassManager(Optimize1qGates())
optimized_qc = pass_manager.run(optimized_qc)
print(optimized_qc)

# Error mitigation: zero-noise extrapolation
from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter
cal_circuits, state_labels = complete_meas_cal(qubit_list=[0,1], circlabel='mcal')
cal_job = execute(cal_circuits, simulator, shots=1024)
cal_results = cal_job.result()
meas_fitter = CompleteMeasFitter(cal_results, state_labels)
mitigated_counts = meas_fitter.filter.apply(result.get_counts(qc))
print("Mitigated counts:", mitigated_counts)

# Debugging: visualize errors in circuit
qc.measure_all()
print(qc.draw())

# Benchmark quantum circuit execution time
import time
start = time.time()
job = execute(qc, simulator, shots=1024)
result = job.result()
end = time.time()
print(f"Execution time: {end - start:.2f} seconds")

# Example: Post-quantum cryptography with pyca/cryptography
pip install cryptography

from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization

# Generate secure RSA key (use post-quantum alternatives in future)
private_key = rsa.generate_private_key(public_exponent=65537, key_size=4096)
private_bytes = private_key.private_bytes(
    encoding=serialization.Encoding.PEM,
    format=serialization.PrivateFormat.TraditionalOpenSSL,
    encryption_algorithm=serialization.NoEncryption()
)
print(private_bytes.decode('utf-8'))

# Secure your quantum API keys (environment variables)
import os
os.environ['AWS_ACCESS_KEY_ID'] = '<YOUR_AWS_ACCESS_KEY_ID>'
os.environ['AWS_SECRET_ACCESS_KEY'] = '<YOUR_AWS_SECRET_ACCESS_KEY>'

# Example configuration file for secure quantum access
[security]
api_key_env = 'AWS_ACCESS_KEY_ID'
api_secret_env = 'AWS_SECRET_ACCESS_KEY'
use_post_quantum_crypto = true

# Validate quantum job permissions
from braket.aws import AwsSession
session = AwsSession()
if not session.profile:
    print("Warning: AWS session profile not set. Check your credentials.")

# Unit test for quantum circuit
import unittest
from qiskit import QuantumCircuit, Aer, execute

class TestQuantumCircuit(unittest.TestCase):
    def test_entanglement(self):
        qc = QuantumCircuit(2)
        qc.h(0)
        qc.cx(0, 1)
        simulator = Aer.get_backend('qasm_simulator')
        job = execute(qc, simulator, shots=1000)
        result = job.result()
        counts = result.get_counts()
        self.assertTrue('00' in counts and '11' in counts)

if __name__ == '__main__':
    unittest.main()

# Integration test: hybrid quantum-classical pipeline
def test_hybrid_pipeline():
    data = np.array([1, 2, 3, 4])
    result = hybrid_optimizer(data)
    assert isinstance(result, float), "Result should be a float."
test_hybrid_pipeline()

# Continuous validation in CI pipeline (GitHub Actions snippet)
name: Quantum Test Pipeline
on: [push]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.10'
      - name: Install dependencies
        run: |
          pip install qiskit
      - name: Run tests
        run: |
          python -m unittest discover tests/

# Debugging: print quantum statevector
from qiskit import Aer, execute
backend = Aer.get_backend('statevector_simulator')
job = execute(qc, backend)
statevector = job.result().get_statevector()
print("Quantum statevector:", statevector)

# Visualize quantum circuit for validation
qc.draw('mpl')