from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit.algorithms import QAOA, VQE

class QuantumReviewOptimizer:
    def __init__(self, n_customers=1000000):
        self.n_qubits = int(np.log2(n_customers)) + 5
        self.quantum_circuit = QuantumCircuit(self.n_qubits)
        
    def create_superposition_state(self):
        """Alle möglichen Timing-Kombinationen überlagern"""
        for qubit in range(self.n_qubits):
            self.quantum_circuit.h(qubit)  # Hadamard Gate
            
    def apply_quantum_oracle(self, optimization_function):
        """Problem-spezifische Quantenlogik"""
        # Implement quantum oracle for review optimization
        pass
        
    def amplitude_amplification(self):
        """Grover-ähnliche Amplifikation optimaler Lösungen"""
        pass

from qiskit.algorithms.optimizers import COBYLA
from qiskit.opflow import PauliSumOp

class ReviewQAOA:
    def __init__(self, problem_hamiltonian):
        self.cost_hamiltonian = problem_hamiltonian
        self.mixer_hamiltonian = self.create_mixer()
        self.optimizer = COBYLA(maxiter=1000)
        
    def create_cost_hamiltonian(self, review_data):
        """Encode optimization problem as quantum Hamiltonian"""
        pauli_list = []
        
        for i, customer in enumerate(review_data):
            # Weight based on predicted review quality
            weight = customer.satisfaction_score * customer.influence_factor
            
            # Pauli-Z operator for binary decision (send/don't send)
            pauli_list.append(('Z', [i], weight))
            
        return PauliSumOp.from_list(pauli_list)
        
    def solve(self, initial_params=None):
        qaoa = QAOA(
            optimizer=self.optimizer,
            reps=3,  # Number of QAOA layers
            initial_point=initial_params
        )
        
        result = qaoa.compute_minimum_eigenvalue(self.cost_hamiltonian)
        return self.decode_solution(result.optimal_point)

from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes

class QuantumSentimentAnalyzer:
    def __init__(self, n_features=128):
        self.n_qubits = int(np.log2(n_features))
        self.feature_map = ZZFeatureMap(
            feature_dimension=n_features,
            reps=2,
            entanglement='full'
        )
        self.ansatz = RealAmplitudes(
            num_qubits=self.n_qubits,
            reps=3
        )
        
    def quantum_kernel(self, x1, x2):
        """Quantum kernel for sentiment similarity"""
        # Create quantum circuit with feature map
        qc = self.feature_map.bind_parameters(x1)
        qc = qc.compose(self.feature_map.bind_parameters(x2).inverse())
        
        # Measure overlap
        return np.abs(execute_circuit(qc))**2
        
    def train_vqe(self, training_data, labels):
        """Train quantum sentiment classifier"""
        def cost_function(params):
            predictions = []
            for data_point in training_data:
                quantum_state = self.prepare_quantum_state(data_point, params)
                prediction = self.measure_expectation(quantum_state)
                predictions.append(prediction)
                
            return mean_squared_error(labels, predictions)
            
        # Classical optimization of quantum parameters
        optimizer = COBYLA()
        result = optimizer.minimize(cost_function, initial_params)
        return result.x

from qiskit_machine_learning.kernels import QuantumKernel

class QuantumReviewClassifier:
    def __init__(self):
        # Define quantum feature map
        self.feature_map = ZZFeatureMap(
            feature_dimension=256,  # High-dimensional customer features
            reps=2,
            entanglement='linear'
        )
        
        # Create quantum kernel
        self.quantum_kernel = QuantumKernel(
            feature_map=self.feature_map,
            quantum_instance=quantum_backend
        )
        
    def fit(self, X_train, y_train):
        """Train quantum SVM"""
        from sklearn.svm import SVC
        
        # Compute quantum kernel matrix
        K_train = self.quantum_kernel.evaluate(x_vec=X_train)
        
        # Train classical SVM with quantum kernel
        self.svm = SVC(kernel='precomputed')
        self.svm.fit(K_train, y_train)
        
    def predict(self, X_test):
        """Quantum-enhanced prediction"""
        K_test = self.quantum_kernel.evaluate(
            x_vec=X_test,
            y_vec=self.X_train
        )
        return self.svm.predict(K_test)

from qiskit.circuit.library import TwoLocal
from qiskit_machine_learning.neural_networks import TwoLayerQNN

class HybridQuantumNeuralNetwork:
    def __init__(self, n_qubits=8):
        self.n_qubits = n_qubits
        
        # Quantum feature map
        self.feature_map = ZZFeatureMap(n_qubits, reps=1)
        
        # Parameterized quantum circuit
        self.ansatz = TwoLocal(
            n_qubits,
            rotation_blocks='ry',
            entanglement_blocks='cz',
            entanglement='circular',
            reps=3
        )
        
        # Create QNN
        self.qnn = TwoLayerQNN(
            num_qubits=n_qubits,
            feature_map=self.feature_map,
            ansatz=self.ansatz,
            observable=observable,
            quantum_instance=quantum_backend
        )
        
    def forward_pass(self, input_data, weights):
        """Quantum forward propagation"""
        quantum_output = self.qnn.forward(input_data, weights)
        return self.post_process(quantum_output)
        
    def backward_pass(self, gradient):
        """Quantum gradient computation"""
        return self.qnn.backward(input_data, weights)[1]  # Parameter gradients

import numpy as np
from qiskit import QuantumCircuit, execute

class QuantumReviewAuthentication:
    def __init__(self):
        self.quantum_channel = QuantumChannel()
        self.classical_channel = ClassicalChannel()
        
    def generate_quantum_key(self, n_bits=256):
        """Generate quantum-secured key for review verification"""
        alice_bits = np.random.randint(2, size=n_bits)
        alice_bases = np.random.randint(2, size=n_bits)
        
        # Prepare quantum states
        qc = QuantumCircuit(1, 1)
        quantum_states = []
        
        for i in range(n_bits):
            qc.reset(0)
            
            # Encode bit
            if alice_bits[i] == 1:
                qc.x(0)
                
            # Choose basis
            if alice_bases[i] == 1:
                qc.h(0)  # Diagonal basis
                
            quantum_states.append(qc.copy())
            
        return quantum_states, alice_bits, alice_bases
        
    def verify_review_integrity(self, review_data, quantum_signature):
        """Quantum-verified review authenticity"""
        # Use quantum signature to verify review hasn't been tampered
        measured_states = self.measure_quantum_signature(quantum_signature)
        expected_states = self.compute_expected_signature(review_data)
        
        return self.compare_quantum_states(measured_states, expected_states)

from dwave.system import DWaveSampler, EmbeddingComposite
import dimod

class QuantumAnnealingOptimizer:
    def __init__(self):
        self.sampler = EmbeddingComposite(DWaveSampler())
        
    def optimize_review_schedule(self, customer_data, constraints):
        """Use quantum annealing for optimal review timing"""
        
        # Formulate as QUBO (Quadratic Unconstrained Binary Optimization)
        Q = self.create_qubo_matrix(customer_data, constraints)
        
        # Submit to D-Wave quantum annealer
        response = self.sampler.sample_qubo(
            Q,
            num_reads=1000,
            annealing_time=20,  # microseconds
            chain_strength=1.0
        )
        
        # Extract optimal solution
        optimal_solution = response.first.sample
        return self.decode_schedule(optimal_solution)
        
    def create_qubo_matrix(self, customers, constraints):
        """Encode optimization problem as QUBO"""
        n = len(customers)
        Q = np.zeros((n, n))
        
        # Objective function: maximize expected review quality
        for i, customer in enumerate(customers):
            Q[i, i] = -customer.expected_review_score
            
        # Constraints: resource limitations, timing conflicts
        for constraint in constraints:
            self.add_constraint_to_qubo(Q, constraint)
            
        return Q

from qiskit.ignis.verification.topological_codes import RepetitionCode
from qiskit.ignis.verification.topological_codes import lookuptable_decoding

class FaultTolerantQuantumReviews:
    def __init__(self, code_distance=5):
        self.code = RepetitionCode(
            distance=code_distance,
            T=1  # Number of syndrome extraction rounds
        )
        self.decoder = lookuptable_decoding
        
    def protected_quantum_computation(self, logical_circuit):
        """Execute quantum computation with error protection"""
        
        # Encode logical qubits
        encoded_circuit = self.code.encode(logical_circuit)
        
        # Add syndrome extraction
        syndrome_circuit = self.code.syndrome_measurement()
        
        # Execute with error correction
        results = []
        for _ in range(self.code.T):
            # Execute syndrome measurement
            syndrome = execute(syndrome_circuit)
            
            # Decode errors
            error_estimate = self.decoder(syndrome)
            
            # Apply correction
            corrected_circuit = self.apply_correction(
                encoded_circuit, error_estimate
            )
            
            results.append(execute(corrected_circuit))
            
        return self.majority_vote(results)

class QuantumReviewNetwork:
    def __init__(self):
        self.quantum_nodes = [
            QuantumNode("business_1", location="Berlin"),
            QuantumNode("business_2", location="Munich"), 
            QuantumNode("business_3", location="Hamburg")
        ]
        self.entanglement_network = QuantumEntanglementNetwork()
        
    def distribute_entanglement(self):
        """Create quantum entanglement between business nodes"""
        for i in range(len(self.quantum_nodes)):
            for j in range(i+1, len(self.quantum_nodes)):
                # Create Bell pairs between nodes
                entangled_pair = self.create_bell_pair()
                self.send_qubit(entangled_pair[0], self.quantum_nodes[i])
                self.send_qubit(entangled_pair[1], self.quantum_nodes[j])
                
    def quantum_consensus_protocol(self, review_data):
        """Achieve consensus on review authenticity via quantum protocol"""
        
        # Quantum voting protocol
        votes = []
        for node in self.quantum_nodes:
            quantum_vote = node.quantum_verify_review(review_data)
            votes.append(quantum_vote)
            
        # Quantum majority gate
        consensus = self.quantum_majority_gate(votes)
        return consensus
        
    def quantum_majority_gate(self, quantum_votes):
        """Implement quantum majority decision"""
        n_votes = len(quantum_votes)
        qc = QuantumCircuit(n_votes + 1, 1)  # +1 for ancilla
        
        # Implement quantum majority logic
        for i, vote_qubit in enumerate(quantum_votes):
            qc.cx(vote_qubit, n_votes)  # XOR all votes into ancilla
            
        # Measure majority result
        qc.measure(n_votes, 0)
        return execute(qc)

# Quantum-inspired classical optimization
class QuantumInspiredOptimizer:
    def __init__(self):
        self.population = self.initialize_quantum_population()
        
    def quantum_crossover(self, parent1, parent2):
        """Quantum-inspired genetic algorithm crossover"""
        # Simulate quantum superposition in classical bits
        superposition_mask = np.random.random(len(parent1)) < 0.5
        
        child = np.where(superposition_mask, parent1, parent2)
        return self.apply_quantum_interference(child)
        
    def evolve_population(self):