Quantum Machine Learning

import numpy as np
from typing import Dict, List, Tuple, Optional, Union
import matplotlib.pyplot as plt
from abc import ABC, abstractmethod

# Quantum computing simulation libraries
try:
    import qiskit
    from qiskit import QuantumCircuit, Aer, execute, transpile
    from qiskit.circuit import Parameter, ParameterVector
    from qiskit.algorithms.optimizers import SPSA, COBYLA
    from qiskit.opflow import X, Y, Z, I, StateFn, CircuitStateFn, PauliExpectation
    from qiskit.utils import QuantumInstance
    QISKIT_AVAILABLE = True
except ImportError:
    QISKIT_AVAILABLE = False
    print("Qiskit not available. Using simulation framework.")

class QuantumMLFramework:
    """
    Comprehensive framework for Quantum Machine Learning
    
    Features:
    - Quantum circuit design and optimization
    - Hybrid classical-quantum algorithms
    - Quantum feature maps and kernels
    - Variational quantum algorithms
    - Quantum data encoding and decoding
    """
    
    def __init__(self, config: Dict):
        self.config = config
        self.num_qubits = config.get('num_qubits', 4)
        self.shots = config.get('shots', 1024)
        
        # Quantum backend setup
        if QISKIT_AVAILABLE:
            self.backend = Aer.get_backend('qasm_simulator')
            self.quantum_instance = QuantumInstance(self.backend, shots=self.shots)
        else:
            self.backend = None
            print("Using classical simulation of quantum circuits")
        
        # Quantum algorithms
        self.algorithms = {
            'qsvm': QuantumSVM(self),
            'qnn': QuantumNeuralNetwork(self),
            'qpca': QuantumPCA(self),
            'qkmeans': QuantumKMeans(self)
        }
        
        # Classical-quantum hybrid optimizers
        self.optimizers = {
            'spsa': SPSA(maxiter=100) if QISKIT_AVAILABLE else None,
            'cobyla': COBYLA(maxiter=200) if QISKIT_AVAILABLE else None
        }
    
    def create_feature_map(self, num_features: int, depth: int = 2, 
                          entanglement: str = 'linear') -> QuantumCircuit:
        """Create quantum feature map for data encoding"""
        
        if not QISKIT_AVAILABLE:
            return self._simulate_feature_map(num_features, depth, entanglement)
        
        # Create parameterized quantum circuit for feature encoding
        feature_map = QuantumCircuit(self.num_qubits)
        
        # Parameters for data encoding
        parameters = ParameterVector('x', num_features)
        
        # Data encoding layers
        for layer in range(depth):
            # Rotation gates for data encoding
            for i in range(min(num_features, self.num_qubits)):
                feature_map.ry(parameters[i], i)
            
            # Entangling gates
            if entanglement == 'linear':
                for i in range(self.num_qubits - 1):
                    feature_map.cnot(i, i + 1)
            elif entanglement == 'full':
                for i in range(self.num_qubits):
                    for j in range(i + 1, self.num_qubits):
                        feature_map.cnot(i, j)
        
        return feature_map
    
    def create_ansatz(self, num_parameters: int, depth: int = 2) -> QuantumCircuit:
        """Create variational ansatz for quantum machine learning"""
        
        if not QISKIT_AVAILABLE:
            return self._simulate_ansatz(num_parameters, depth)
        
        ansatz = QuantumCircuit(self.num_qubits)
        
        # Variational parameters
        theta = ParameterVector('θ', num_parameters)
        param_idx = 0
        
        for layer in range(depth):
            # Rotation gates with variational parameters
            for i in range(self.num_qubits):
                if param_idx < num_parameters:
                    ansatz.ry(theta[param_idx], i)
                    param_idx += 1
                if param_idx < num_parameters:
                    ansatz.rz(theta[param_idx], i)
                    param_idx += 1
            
            # Entangling layer
            for i in range(self.num_qubits - 1):
                ansatz.cnot(i, i + 1)
        
        return ansatz
    
    def quantum_kernel(self, x1: np.ndarray, x2: np.ndarray, 
                      feature_map: QuantumCircuit) -> float:
        """Compute quantum kernel between two data points"""
        
        if not QISKIT_AVAILABLE:
            return self._simulate_quantum_kernel(x1, x2)
        
        # Create quantum circuit for kernel computation
        kernel_circuit = QuantumCircuit(self.num_qubits, 1)
        
        # Encode first data point
        bound_circuit1 = feature_map.bind_parameters(x1)
        kernel_circuit.compose(bound_circuit1, inplace=True)
        
        # Encode second data point (conjugate)
        bound_circuit2 = feature_map.bind_parameters(x2)
        kernel_circuit.compose(bound_circuit2.inverse(), inplace=True)
        
        # Measure overlap
        kernel_circuit.measure_all()
        
        # Execute circuit
        job = execute(kernel_circuit, self.backend, shots=self.shots)
        result = job.result()
        counts = result.get_counts()
        
        # Calculate kernel value (probability of measuring |0...0⟩)
        zero_state = '0' * self.num_qubits
        kernel_value = counts.get(zero_state, 0) / self.shots
        
        return kernel_value

class QuantumSVM:
    """Quantum Support Vector Machine implementation"""
    
    def __init__(self, framework: QuantumMLFramework):
        self.framework = framework
        self.feature_map = None
        self.training_data = None
        self.training_labels = None
        self.support_vectors = None
        self.alpha = None
        
    def fit(self, X: np.ndarray, y: np.ndarray) -> Dict:
        """Train quantum SVM"""
        
        print("Training Quantum SVM...")
        
        # Create quantum feature map
        num_features = X.shape[1]
        self.feature_map = self.framework.create_feature_map(num_features)
        
        # Store training data
        self.training_data = X
        self.training_labels = y
        
        # Compute quantum kernel matrix
        kernel_matrix = self._compute_kernel_matrix(X)
        
        # Solve SVM optimization (classical part)
        self.alpha, self.support_vectors = self._solve_svm_optimization(kernel_matrix, y)
        
        # Calculate training accuracy
        train_predictions = self.predict(X)
        train_accuracy = np.mean(train_predictions == y)
        
        return {
            'training_accuracy': train_accuracy,
            'num_support_vectors': len(self.support_vectors),
            'kernel_matrix_shape': kernel_matrix.shape
        }
    
    def predict(self, X: np.ndarray) -> np.ndarray:
        """Make predictions using trained quantum SVM"""
        
        if self.support_vectors is None:
            raise ValueError("Model must be trained before making predictions")
        
        predictions = []
        
        for x in X:
            # Compute kernel values with support vectors
            kernel_values = []
            for sv_idx in self.support_vectors:
                sv = self.training_data[sv_idx]
                kernel_val = self.framework.quantum_kernel(x, sv, self.feature_map)
                kernel_values.append(kernel_val)
            
            # Compute decision function
            decision_value = sum(
                self.alpha[i] * self.training_labels[sv_idx] * kernel_values[i]
                for i, sv_idx in enumerate(self.support_vectors)
            )
            
            # Binary classification
            prediction = 1 if decision_value > 0 else -1
            predictions.append(prediction)
        
        return np.array(predictions)
    
    def _compute_kernel_matrix(self, X: np.ndarray) -> np.ndarray:
        """Compute quantum kernel matrix for training data"""
        
        n_samples = X.shape[0]
        kernel_matrix = np.zeros((n_samples, n_samples))
        
        for i in range(n_samples):
            for j in range(i, n_samples):
                kernel_val = self.framework.quantum_kernel(X[i], X[j], self.feature_map)
                kernel_matrix[i, j] = kernel_val
                kernel_matrix[j, i] = kernel_val  # Symmetric
        
        return kernel_matrix
    
    def _solve_svm_optimization(self, kernel_matrix: np.ndarray, 
                               y: np.ndarray) -> Tuple[np.ndarray, List[int]]:
        """Solve SVM optimization problem (simplified)"""
        
        # Simplified SVM optimization (in practice, use SMO algorithm)
        n_samples = len(y)
        
        # Initialize alpha coefficients
        alpha = np.random.uniform(0, 1, n_samples)
        alpha = alpha / np.sum(alpha)  # Normalize
        
        # Simple iterative optimization (placeholder)
        for iteration in range(100):
            # Update alpha based on gradient descent (simplified)
            gradient = self._compute_gradient(alpha, kernel_matrix, y)
            alpha = alpha - 0.01 * gradient
            alpha = np.clip(alpha, 0, 1)  # Constraints
        
        # Identify support vectors (non-zero alpha)
        support_vectors = [i for i, a in enumerate(alpha) if a > 1e-6]
        
        return alpha, support_vectors
    
    def _compute_gradient(self, alpha: np.ndarray, kernel_matrix: np.ndarray, 
                         y: np.ndarray) -> np.ndarray:
        """Compute gradient for SVM optimization"""
        
        # Simplified gradient computation
        gradient = np.ones(len(alpha))
        
        for i in range(len(alpha)):
            gradient[i] -= sum(
                alpha[j] * y[i] * y[j] * kernel_matrix[i, j]
                for j in range(len(alpha))
            )
        
        return gradient

class QuantumNeuralNetwork:
    """Variational Quantum Neural Network"""
    
    def __init__(self, framework: QuantumMLFramework):
        self.framework = framework
        self.num_layers = 3
        self.num_parameters = self.framework.num_qubits * self.num_layers * 2
        self.parameters = None
        self.ansatz = None
        
    def fit(self, X: np.ndarray, y: np.ndarray, epochs: int = 100) -> Dict:
        """Train quantum neural network"""
        
        print("Training Quantum Neural Network...")
        
        # Create quantum circuit ansatz
        self.ansatz = self.framework.create_ansatz(self.num_parameters, self.num_layers)
        
        # Initialize parameters
        self.parameters = np.random.uniform(0, 2*np.pi, self.num_parameters)
        
        # Training history
        loss_history = []
        accuracy_history = []
        
        # Training loop
        for epoch in range(epochs):
            # Forward pass
            predictions = self._forward_pass(X)
            
            # Compute loss
            loss = self._compute_loss(predictions, y)
            loss_history.append(loss)
            
            # Compute accuracy
            binary_predictions = (predictions > 0.5).astype(int)
            accuracy = np.mean(binary_predictions == y)
            accuracy_history.append(accuracy)
            
            # Backward pass (parameter update)
            self._update_parameters(X, y, learning_rate=0.01)
            
            if epoch % 10 == 0:
                print(f"Epoch {epoch}: Loss = {loss:.4f}, Accuracy = {accuracy:.4f}")
        
        return {
            'final_loss': loss_history[-1],
            'final_accuracy': accuracy_history[-1],
            'loss_history': loss_history,
            'accuracy_history': accuracy_history
        }
    
    def predict(self, X: np.ndarray) -> np.ndarray:
        """Make predictions using trained QNN"""
        
        if self.parameters is None:
            raise ValueError("Model must be trained before making predictions")
        
        return self._forward_pass(X)
    
    def _forward_pass(self, X: np.ndarray) -> np.ndarray:
        """Forward pass through quantum neural network"""
        
        predictions = []
        
        for x in X:
            # Create quantum circuit with data encoding and ansatz
            circuit = QuantumCircuit(self.framework.num_qubits, 1)
            
            # Data encoding (simplified)
            for i, val in enumerate(x[:self.framework.num_qubits]):
                circuit.ry(val, i)
            
            # Apply parameterized ansatz
            if QISKIT_AVAILABLE:
                bound_ansatz = self.ansatz.bind_parameters(self.parameters)
                circuit.compose(bound_ansatz, inplace=True)
            
            # Measurement
            circuit.measure(0, 0)  # Measure first qubit
            
            # Execute circuit
            if QISKIT_AVAILABLE:
                job = execute(circuit, self.framework.backend, shots=self.framework.shots)
                result = job.result()
                counts = result.get_counts()
                
                # Convert to probability
                prob_1 = counts.get('1', 0) / self.framework.shots
            else:
                # Simulate quantum measurement
                prob_1 = self._simulate_measurement(x)
            
            predictions.append(prob_1)
        
        return np.array(predictions)
    
    def _compute_loss(self, predictions: np.ndarray, targets: np.ndarray) -> float:
        """Compute loss function"""
        
        # Binary cross-entropy loss
        epsilon = 1e-15  # Prevent log(0)
        predictions = np.clip(predictions, epsilon, 1 - epsilon)
        
        loss = -np.mean(
            targets * np.log(predictions) + (1 - targets) * np.log(1 - predictions)
        )
        
        return loss
    
    def _update_parameters(self, X: np.ndarray, y: np.ndarray, learning_rate: float):
        """Update quantum neural network parameters"""
        
        # Parameter-shift rule for gradient computation
        gradients = np.zeros_like(self.parameters)
        
        for i in range(len(self.parameters)):
            # Forward pass with positive shift
            self.parameters[i] += np.pi / 2
            pred_plus = self._forward_pass(X)
            loss_plus = self._compute_loss(pred_plus, y)
            
            # Forward pass with negative shift
            self.parameters[i] -= np.pi
            pred_minus = self._forward_pass(X)
            loss_minus = self._compute_loss(pred_minus, y)
            
            # Compute gradient using parameter-shift rule
            gradients[i] = 0.5 * (loss_plus - loss_minus)
            
            # Restore original parameter value
            self.parameters[i] += np.pi / 2
        
        # Update parameters
        self.parameters -= learning_rate * gradients
    
    def _simulate_measurement(self, x: np.ndarray) -> float:
        """Simulate quantum measurement (when Qiskit not available)"""
        
        # Simplified simulation of quantum neural network output
        # In practice, this would involve full quantum state simulation
        
        # Use a classical neural network as approximation
        weighted_sum = np.dot(x[:len(self.parameters)], self.parameters[:len(x)])
        probability = 1 / (1 + np.exp(-weighted_sum))  # Sigmoid activation
        
        return probability

class QuantumPCA:
    """Quantum Principal Component Analysis"""
    
    def __init__(self, framework: QuantumMLFramework):
        self.framework = framework
        self.eigenvalues = None
        self.eigenvectors = None
        self.num_components = None
        
    def fit(self, X: np.ndarray, num_components: int = 2) -> Dict:
        """Perform quantum PCA on data"""
        
        print("Performing Quantum PCA...")
        
        self.num_components = num_components
        
        # In a real quantum PCA implementation, this would use:
        # 1. Quantum phase estimation for eigenvalue computation
        # 2. Quantum matrix exponentiation
        # 3. Quantum state preparation and measurement
        
        # For simulation, we use classical PCA with quantum-inspired optimization
        covariance_matrix = np.cov(X.T)
        
        # Simulate quantum eigenvalue estimation
        eigenvalues, eigenvectors = self._quantum_eigendecomposition(covariance_matrix)
        
        # Sort by eigenvalues (descending)
        idx = np.argsort(eigenvalues)[::-1]
        self.eigenvalues = eigenvalues[idx]
        self.eigenvectors = eigenvectors[:, idx]
        
        # Keep only top components
        self.eigenvalues = self.eigenvalues[:num_components]
        self.eigenvectors = self.eigenvectors[:, :num_components]
        
        # Calculate explained variance ratio
        explained_variance_ratio = self.eigenvalues / np.sum(eigenvalues)
        
        return {
            'eigenvalues': self.eigenvalues,
            'explained_variance_ratio': explained_variance_ratio,
            'total_variance_explained': np.sum(explained_variance_ratio)
        }
    
    def transform(self, X: np.ndarray) -> np.ndarray:
        """Transform data to quantum principal component space"""
        
        if self.eigenvectors is None:
            raise ValueError("Model must be fitted before transformation")
        
        # Project data onto principal components
        X_centered = X - np.mean(X, axis=0)
        X_transformed = np.dot(X_centered, self.eigenvectors)
        
        return X_transformed
    
    def _quantum_eigendecomposition(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        """Simulate quantum eigenvalue decomposition"""
        
        # In a real quantum implementation, this would use:
        # - Quantum phase estimation algorithm
        # - Variational quantum eigensolver (VQE)
        # - Quantum approximate optimization algorithm (QAOA)
        
        # For now, use classical eigendecomposition
        eigenvalues, eigenvectors = np.linalg.eigh(matrix)
        
        return eigenvalues, eigenvectors

class QuantumKMeans:
    """Quantum k-means clustering algorithm"""
    
    def __init__(self, framework: QuantumMLFramework):
        self.framework = framework
        self.centroids = None
        self.labels = None
        self.k = None
        
    def fit(self, X: np.ndarray, k: int, max_iters: int = 100) -> Dict:
        """Perform quantum k-means clustering"""
        
        print(f"Performing Quantum k-means with k={k}...")
        
        self.k = k
        n_samples, n_features = X.shape
        
        # Initialize centroids randomly
        self.centroids = X[np.random.choice(n_samples, k, replace=False)]
        
        # Convergence tracking
        inertia_history = []
        
        for iteration in range(max_iters):
            # Assign points to clusters using quantum distance computation
            labels = self._quantum_assign_clusters(X)
            
            # Update centroids
            new_centroids = np.zeros_like(self.centroids)
            for i in range(k):
                cluster_points = X[labels == i]
                if len(cluster_points) > 0:
                    new_centroids[i] = np.mean(cluster_points, axis=0)
                else:
                    new_centroids[i] = self.centroids[i]  # Keep old centroid
            
            # Check convergence
            centroid_shift = np.sum(np.linalg.norm(new_centroids - self.centroids, axis=1))
            self.centroids = new_centroids
            
            # Calculate inertia (within-cluster sum of squares)
            inertia = self._calculate_inertia(X, labels)
            inertia_history.append(inertia)
            
            if centroid_shift < 1e-6:
                print(f"Converged after {iteration + 1} iterations")
                break
        
        self.labels = labels
        
        return {
            'n_iterations': iteration + 1,
            'final_inertia': inertia_history[-1],
            'inertia_history': inertia_history,
            'centroids': self.centroids
        }
    
    def predict(self, X: np.ndarray) -> np.ndarray:
        """Assign new points to clusters"""
        
        if self.centroids is None:
            raise ValueError("Model must be fitted before prediction")
        
        return self._quantum_assign_clusters(X)
    
    def _quantum_assign_clusters(self, X: np.ndarray) -> np.ndarray:
        """Assign points to clusters using quantum distance computation"""
        
        labels = np.zeros(len(X), dtype=int)
        
        for i, point in enumerate(X):
            # Compute quantum distances to all centroids
            distances = []
            
            for centroid in self.centroids:
                # Quantum distance computation
                # In real implementation, this would use quantum swap test
                # or other quantum distance algorithms
                quantum_distance = self._quantum_distance(point, centroid)
                distances.append(quantum_distance)
            
            # Assign to nearest centroid
            labels[i] = np.argmin(distances)
        
        return labels
    
    def _quantum_distance(self, x1: np.ndarray, x2: np.ndarray) -> float:
        """Compute quantum distance between two points"""
        
        if QISKIT_AVAILABLE:
            # Use quantum feature map and kernel for distance
            feature_map = self.framework.create_feature_map(len(x1))
            kernel_value = self.framework.quantum_kernel(x1, x2, feature_map)
            
            # Convert kernel to distance
            # K(x,y) = ⟨φ(x)|φ(y)⟩, distance = ||φ(x) - φ(y)||²
            distance = 2 * (1 - kernel_value)
        else:
            # Classical simulation of quantum distance
            distance = np.linalg.norm(x1 - x2)
        
        return distance
    
    def _calculate_inertia(self, X: np.ndarray, labels: np.ndarray) -> float:
        """Calculate within-cluster sum of squares"""
        
        inertia = 0.0
        
        for i in range(self.k):
            cluster_points = X[labels == i]
            if len(cluster_points) > 0:
                cluster_inertia = np.sum(
                    [self._quantum_distance(point, self.centroids[i])**2 
                     for point in cluster_points]
                )
                inertia += cluster_inertia
        
        return inertia

# Simulation functions when Qiskit is not available
def simulate_quantum_computation():
    """Demonstrate quantum ML concepts with classical simulation"""
    
    print("Simulating Quantum Machine Learning...")
    
    # Generate sample data
    np.random.seed(42)
    X = np.random.randn(100, 4)
    y = (X[:, 0] + X[:, 1] > 0).astype(int)
    
    # Initialize quantum ML framework
    config = {
        'num_qubits': 4,
        'shots': 1024
    }
    
    framework = QuantumMLFramework(config)
    
    # Test Quantum SVM
    print("\n=== Quantum SVM ===")
    qsvm = framework.algorithms['qsvm']
    svm_result = qsvm.fit(X, y)
    print(f"Training accuracy: {svm_result['training_accuracy']:.3f}")
    
    # Test Quantum Neural Network
    print("\n=== Quantum Neural Network ===")
    qnn = framework.algorithms['qnn']
    qnn_result = qnn.fit(X, y, epochs=20)
    print(f"Final accuracy: {qnn_result['final_accuracy']:.3f}")
    
    # Test Quantum PCA
    print("\n=== Quantum PCA ===")
    qpca = framework.algorithms['qpca']
    pca_result = qpca.fit(X, num_components=2)
    print(f"Variance explained: {pca_result['total_variance_explained']:.3f}")
    
    # Test Quantum K-means
    print("\n=== Quantum K-means ===")
    qkmeans = framework.algorithms['qkmeans']
    kmeans_result = qkmeans.fit(X, k=2)
    print(f"Final inertia: {kmeans_result['final_inertia']:.3f}")
    
    return {
        'qsvm': svm_result,
        'qnn': qnn_result,
        'qpca': pca_result,
        'qkmeans': kmeans_result
    }

# Example usage
if __name__ == "__main__":
    results = simulate_quantum_computation()
    print("\nQuantum ML simulation completed!")