class QuantumPrecisionTracker {
    constructor() {
        this.quantumState = {
            superposition: true,
            entanglement: true,
            coherence: true,
            decoherence: 0.001
        };
        
        this.neuralNetwork = {
            layers: 8,
            neuronsPerLayer: 1024,
            learningRate: 0.0001,
            trainingEpochs: 10000,
            activationFunction: 'QuantumReLU'
        };
        
        this.trackingConfig = {
            precisionLevel: 'QuantumNanoscale',
            predictionHorizon: 5.0,
            updateFrequency: 1000,
            errorTolerance: 0.0001,
            confidenceThreshold: 0.9999
        };
        
        this.weaponSystems = {
            assaultRifle: {
                quantumBoost: 1.8,
                neuralPrediction: true,
                adaptiveRecoil: 0.95,
                bulletVelocity: 950,
                effectiveRange: 600,
                penetrationPower: 0.85
            },
            sniperRifle: {
                quantumBoost: 2.5,
                neuralPrediction: true,
                adaptiveRecoil: 0.99,
                bulletVelocity: 1200,
                effectiveRange: 2000,
                penetrationPower: 0.95
            },
            smg: {
                quantumBoost: 1.6,
                neuralPrediction: true,
                adaptiveRecoil: 0.88,
                bulletVelocity: 420,
                effectiveRange: 300,
                penetrationPower: 0.7
            },
            shotgun: {
                quantumBoost: 1.4,
                neuralPrediction: true,
                adaptiveRecoil: 0.8,
                bulletVelocity: 350,
                effectiveRange: 100,
                penetrationPower: 0.6
            }
        };
        
        this.targetData = {
            currentTarget: null,
            targetHistory: new Map(),
            predictionCache: new Map(),
            hitStatistics: {
                totalShots: 0,
                successfulHits: 0,
                headshotCount: 0,
                movingTargetHits: 0
            }
        };
        
        this.initializeQuantumEngine();
    }
    
    initializeQuantumEngine() {
        this.quantumProcessor = new QuantumProcessor();
        this.neuralEngine = new NeuralPredictionEngine();
        this.initializeQuantumNeuralNetwork();
        this.calibrateSystem();
    }
    
    initializeQuantumNeuralNetwork() {
        this.qnnLayers = [];
        for (let i = 0; i < this.neuralNetwork.layers; i++) {
            this.qnnLayers.push(new QuantumNeuralLayer(
                this.neuralNetwork.neuronsPerLayer,
                this.neuralNetwork.activationFunction
            ));
        }
        
        this.trainingOptimizer = new QuantumGradientDescent({
            learningRate: this.neuralNetwork.learningRate,
            momentum: 0.9,
            quantization: true
        });
    }
    
    calibrateSystem() {
        const calibrationData = this.generateCalibrationPatterns();
        this.quantumCalibration = this.performQuantumCalibration(calibrationData);
        this.neuralCalibration = this.trainNeuralNetwork(calibrationData);
    }
    
    acquireQuantumTarget(targets, environment) {
        if (!targets.length) return null;
        
        const quantumScores = new Map();
        const currentTime = performance.now();
        
        for (const target of targets) {
            if (this.isValidTarget(target)) {
                const quantumScore = this.calculateQuantumTargetScore(target, environment);
                quantumScores.set(target, quantumScore);
                
                this.updateTargetHistory(target, currentTime);
            }
        }
        
        if (quantumScores.size === 0) return null;
        
        const bestTarget = this.selectOptimalTarget(quantumScores);
        this.targetData.currentTarget = bestTarget;
        
        return bestTarget;
    }
    
    calculateQuantumTargetScore(target, environment) {
        let score = 0;
        
        const quantumPrediction = this.quantumPredictTargetPosition(target);
        const neuralPrediction = this.neuralPredictTargetBehavior(target);
        
        const distanceFactor = this.calculateDistanceFactor(target.distance);
        score += distanceFactor * 0.25;
        
        const visibilityScore = this.calculateVisibilityScore(target, environment);
        score += visibilityScore * 0.20;
        
        const threatAssessment = this.assessThreatLevel(target);
        score += threatAssessment * 0.25;
        
        const movementPenalty = this.calculateMovementPenalty(target);
        score *= (1 - movementPenalty);
        
        const quantumBoost = this.calculateQuantumUncertaintyReduction(target);
        score *= quantumBoost;
        
        const neuralConfidence = this.calculateNeuralConfidence(target);
        score *= neuralConfidence;
        
        return Math.min(1.0, score);
    }
    
    quantumPredictTargetPosition(target) {
        const qubitCount = 512;
        const quantumState = this.initializeQuantumState(qubitCount);
        
        const positionQubits = this.encodePositionToQubits(target.position, quantumState);
        const velocityQubits = this.encodeVelocityToQubits(target.velocity, quantumState);
        
        this.applyQuantumGates(positionQubits, velocityQubits);
        
        const predictedPosition = this.measureQuantumState(positionQubits);
        const uncertainty = this.calculateQuantumUncertainty(positionQubits);
        
        return {
            position: predictedPosition,
            uncertainty: uncertainty,
            timestamp: performance.now()
        };
    }
    
    neuralPredictTargetBehavior(target) {
        const inputFeatures = this.extractNeuralFeatures(target);
        let hiddenState = inputFeatures;
        
        for (const layer of this.qnnLayers) {
            hiddenState = layer.forwardPass(hiddenState);
        }
        
        const behaviorPrediction = this.decodeNeuralOutput(hiddenState);
        
        return {
            nextPosition: behaviorPrediction.position,
            expectedActions: behaviorPrediction.actions,
            confidence: behaviorPrediction.confidence
        };
    }
    
    trackBulletWithQuantumPrecision(shooterPos, target, weaponType, bulletParams) {
        if (!this.targetData.currentTarget) return shooterPos;
        
        const weaponConfig = this.weaponSystems[weaponType];
        if (!weaponConfig) return shooterPos;
        
        const quantumPrediction = this.quantumPredictTargetPosition(target);
        const neuralPrediction = this.neuralPredictTargetBehavior(target);
        
        const combinedPrediction = this.fusePredictions(
            quantumPrediction, 
            neuralPrediction
        );
        
        const optimizedTrajectory = this.calculateQuantumOptimalTrajectory(
            shooterPos,
            combinedPrediction.position,
            weaponConfig,
            bulletParams
        );
        
        this.updateHitProbability(target, combinedPrediction.confidence);
        
        return optimizedTrajectory;
    }
    
    calculateQuantumOptimalTrajectory(start, predictedEnd, weaponConfig, bulletParams) {
        const quantumPaths = this.generateQuantumPathOptions(start, predictedEnd);
        const optimalPath = this.selectOptimalQuantumPath(quantumPaths, weaponConfig);
        
        const correctedPath = this.applyQuantumCorrections(optimalPath, bulletParams);
        
        return correctedPath;
    }
    
    generateQuantumPathOptions(start, end) {
        const paths = [];
        const numPaths = 64;
        
        for (let i = 0; i < numPaths; i++) {
            const path = this.generateSingleQuantumPath(start, end, i);
            paths.push(path);
        }
        
        return paths;
    }
    
    generateSingleQuantumPath(start, end, pathIndex) {
        const controlPoints = this.calculateQuantumControlPoints(start, end, pathIndex);
        const quantumPath = new QuantumBezierCurve(controlPoints);
        
        return {
            path: quantumPath,
            energy: this.calculatePathEnergy(quantumPath),
            probability: this.calculatePathProbability(quantumPath)
        };
    }
    
    selectOptimalQuantumPath(paths, weaponConfig) {
        let bestPath = null;
        let bestScore = -Infinity;
        
        for (const path of paths) {
            const score = this.calculatePathScore(path, weaponConfig);
            
            if (score > bestScore) {
                bestScore = score;
                bestPath = path;
            }
        }
        
        return bestPath;
    }
    
    calculatePathScore(path, weaponConfig) {
        let score = 0;
        
        score += (1 - path.energy) * 0.4;
        score += path.probability * 0.4;
        
        const smoothness = this.calculatePathSmoothness(path.path);
        score += smoothness * 0.2;
        
        const weaponBonus = weaponConfig.quantumBoost * 0.1;
        score += weaponBonus;
        
        return score;
    }
    
    applyQuantumCorrections(path, bulletParams) {
        const correctedPath = path.path.clone();
        
        this.applyAirResistanceCorrection(correctedPath, bulletParams);
        this.applyGravityCorrection(correctedPath);
        this.applyWindCorrection(correctedPath);
        this.applyQuantumUncertaintyCorrection(correctedPath);
        
        return correctedPath.getPoints(100);
    }
    
    updateHitProbability(target, confidence) {
        const baseProbability = this.trackingConfig.confidenceThreshold;
        const distanceFactor = Math.max(0.1, 1 - (target.distance / 2000));
        const movementFactor = this.calculateMovementStability(target);
        
        const finalProbability = baseProbability * confidence * distanceFactor * movementFactor;
        
        this.targetData.hitProbability = Math.min(0.9999, finalProbability);
        
        this.updateStatistics(finalProbability > 0.95);
    }
    
    calculateMovementStability(target) {
        const speed = Math.sqrt(
            target.velocity.x ** 2 + 
            target.velocity.y ** 2 + 
            target.velocity.z ** 2
        );
        
        return Math.max(0.1, 1 - (speed / 100));
    }
    
    getQuantumTrackingStatus() {
        return {
            quantumState: this.quantumState,
            trackingConfig: this.trackingConfig,
            currentTarget: this.targetData.currentTarget,
            hitProbability: this.targetData.hitProbability,
            statistics: this.targetData.hitStatistics,
            neuralNetwork: {
                layers: this.neuralNetwork.layers,
                trainingEpochs: this.neuralNetwork.trainingEpochs,
                currentAccuracy: this.calculateNetworkAccuracy()
            }
        };
    }
    
    calculateNetworkAccuracy() {
        if (this.targetData.hitStatistics.totalShots === 0) return 0;
        
        return this.targetData.hitStatistics.successfulHits / 
               this.targetData.hitStatistics.totalShots;
    }
    
    updateStatistics(hitSuccess) {
        this.targetData.hitStatistics.totalShots++;
        
        if (hitSuccess) {
            this.targetData.hitStatistics.successfulHits++;
        }
    }
    
    // 量子计算辅助类
    class QuantumProcessor {
        constructor() {
            this.qubitRegister = new Map();
        }
        
        initializeQubit(qubitId) {
            this.qubitRegister.set(qubitId, {
                state: [1, 0],
                phase: 0,
                entanglement: null
            });
        }
        
        applyQuantumGate(qubitId, gateMatrix) {
            const qubit = this.qubitRegister.get(qubitId);
            if (qubit) {
                qubit.state = this.matrixMultiply(gateMatrix, qubit.state);
            }
        }
        
        measureQubit(qubitId) {
            const qubit = this.qubitRegister.get(qubitId);
            if (qubit) {
                const probability = Math.abs(qubit.state[0]) ** 2;
                return Math.random() < probability ? 0 : 1;
            }
            return 0;
        }
    }
    
    class QuantumNeuralLayer {
        constructor(neuronCount, activation) {
            this.neurons = neuronCount;
            this.activation = activation;
            this.weights = this.initializeQuantumWeights();
        }
        
        forwardPass(input) {
            const weightedSum = this.matrixMultiply(this.weights, input);
            return this.applyActivation(weightedSum);
        }
        
        applyActivation(x) {
            switch (this.activation) {
                case 'QuantumReLU':
                    return x.map(val => Math.max(0, val));
                case 'QuantumSigmoid':
                    return x.map(val => 1 / (1 + Math.exp(-val)));
                default:
                    return x;
            }
        }
    }
}

const quantumTracker = new QuantumPrecisionTracker();

quantumTracker.initializeQuantumEngine();

export default quantumTracker