Quantum Machine Learning

// Quantum Machine Learning Framework class QuantumMachineLearning { constructor() { this.qubits = []; this.gates = []; this.circuits = []; this.algorithms = []; } // Quantum Neural Network quantumNeuralNetwork(input, weights, layers) { const qnn = { input: input, weights: weights, layers: layers, output: null, fidelity: 0, entanglement: null }; // Quantum State Preparation const quantumState = this.prepareQuantumState(input); // Quantum Circuit Construction const circuit = this.constructCircuit(weights, layers); // Quantum Gate Application const processedState = this.applyGates(quantumState, circuit); // Measurement and Output qnn.output = this.measureOutput(processedState); // Quality Assessment qnn.fidelity = this.calculateFidelity(qnn.output); qnn.entanglement = this.measureEntanglement(processedState); return qnn; } // Quantum Optimization quantumOptimization(problem, parameters) { const optimization = { problem: problem, parameters: parameters, solution: null, cost: null, iterations: 0, convergence: false }; // Problem Encoding const encodedProblem = this.encodeProblem(problem); // Quantum Circuit Design const circuit = this.designCircuit(encodedProblem, parameters); // Variational Optimization optimization.solution = this.variationalOptimize(circuit, parameters); // Cost Evaluation optimization.cost = this.evaluateCost(optimization.solution); // Convergence Check optimization.convergence = this.checkConvergence(optimization.cost); return optimization; } // Quantum Feature Map quantumFeatureMap(data, encoding) { const featureMap = { data: data, encoding: encoding, quantumState: null, features: [], dimension: 0 }; // Data Preprocessing const preprocessed = this.preprocessData(data); // Quantum Encoding featureMap.quantumState = this.encodeToQuantum(preprocessed, encoding); // Feature Extraction featureMap.features = this.extractFeatures(featureMap.quantumState); // Dimension Analysis featureMap.dimension = this.analyzeDimension(featureMap.features); return featureMap; } // Quantum Kernel Method quantumKernelMethod(data, kernel) { const kernelMethod = { data: data, kernel: kernel, matrix: null, eigenvalues: [], classification: null }; // Kernel Matrix Construction kernelMethod.matrix = this.constructKernelMatrix(data, kernel); // Eigenvalue Analysis kernelMethod.eigenvalues = this.analyzeEigenvalues(kernelMethod.matrix); // Classification kernelMethod.classification = this.classifyData(kernelMethod.matrix); return kernelMethod; } // Variational Quantum Eigensolver variationalQuantumEigensolver(hamiltonian, ansatz) { const vqe = { hamiltonian: hamiltonian, ansatz: ansatz, groundState: null, energy: null, parameters: [], convergence: false }; // Ansatz Construction const circuit = this.constructAnsatz(ansatz); // Parameter Optimization vqe.parameters = this.optimizeParameters(circuit, hamiltonian); // Ground State Calculation vqe.groundState = this.calculateGroundState(circuit, vqe.parameters); // Energy Evaluation vqe.energy = this.evaluateEnergy(vqe.groundState, hamiltonian); // Convergence Analysis vqe.convergence = this.analyzeConvergence(vqe.energy); return vqe; } // Quantum Generative Model quantumGenerativeModel(data, model) { const generativeModel = { data: data, model: model, generated: null, fidelity: 0, diversity: 0 }; // Model Training const trainedModel = this.trainModel(data, model); // Data Generation generativeModel.generated = this.generateData(trainedModel); // Quality Assessment generativeModel.fidelity = this.assessFidelity(generativeModel.generated, data); generativeModel.diversity = this.assessDiversity(generativeModel.generated); return generativeModel; } }