/**
 * Swarm-based Quantum Circuit Exploration
 *
 * Uses swarm intelligence and AgentDB to explore quantum circuit designs,
 * learn optimal ansatz patterns, and discover novel circuit architectures.
 *
 * Features:
 * - Multi-agent circuit exploration
 * - Pattern recognition with AgentDB vector search
 * - Self-learning optimal circuit depths
 * - Adaptive ansatz generation
 */

import AgentDB from 'agentdb';
import OpenAI from 'openai';

export interface CircuitExplorationConfig {
  numQubits: number;
  problemType: 'maxcut' | 'vqe' | 'qaoa' | 'custom';
  swarmSize: number;
  maxDepth: number;
  explorationSteps: number;
  learningRate: number;
  memorySize: number;
  useOpenRouter?: boolean;
  openrouterApiKey?: string;
}

export interface QuantumCircuit {
  gates: Gate[];
  depth: number;
  numQubits: number;
  parameters: number[];
  performance: number;
  metadata: CircuitMetadata;
}

export interface Gate {
  type: 'RX' | 'RY' | 'RZ' | 'CNOT' | 'CZ' | 'H' | 'T' | 'S';
  qubits: number[];
  parameter?: number;
}

export interface CircuitMetadata {
  ansatzType: string;
  problemType: string;
  twoQubitGateCount: number;
  expressibility: number;
  entanglingCapability: number;
  circuitId: string;
}

export interface SwarmAgent {
  id: string;
  position: QuantumCircuit;
  velocity: number[];
  bestPosition: QuantumCircuit;
  bestPerformance: number;
}

export interface ExplorationResult {
  bestCircuit: QuantumCircuit;
  bestPerformance: number;
  explorationHistory: QuantumCircuit[];
  learnedPatterns: CircuitPattern[];
  convergenceData: ConvergenceData;
  executionTime: number;
}

export interface CircuitPattern {
  pattern: string;
  frequency: number;
  averagePerformance: number;
  embedding: number[];
}

export interface ConvergenceData {
  performanceHistory: number[];
  diversityHistory: number[];
  converged: boolean;
  convergenceStep: number;
}

/**
 * Swarm-based Circuit Explorer with AgentDB
 */
export class SwarmCircuitExplorer {
  private config: CircuitExplorationConfig;
  private agentDB: any;
  private openai?: OpenAI;
  private swarm: SwarmAgent[] = [];
  private globalBest?: QuantumCircuit;
  private explorationHistory: QuantumCircuit[] = [];

  constructor(config: CircuitExplorationConfig) {
    this.config = config;
    this.initializeAgentDB();

    if (config.useOpenRouter && config.openrouterApiKey) {
      this.openai = new OpenAI({
        baseURL: 'https://openrouter.ai/api/v1',
        apiKey: config.openrouterApiKey,
      });
    }
  }

  /**
   * Initialize AgentDB for pattern storage and retrieval
   */
  private async initializeAgentDB(): Promise<void> {
    this.agentDB = await AgentDB.getInstance({
      name: 'quantum-circuit-patterns',
      dimensions: 128,
      metric: 'cosine',
      quantization: { enabled: true, bits: 8 }
    });

    // Enable learning for adaptive optimization
    await this.agentDB.enableLearning({
      algorithm: 'decision-transformer',
      learningRate: this.config.learningRate
    });
  }

  /**
   * Explore quantum circuit space using swarm intelligence
   */
  async explore(): Promise<ExplorationResult> {
    const startTime = Date.now();

    // Initialize swarm
    this.initializeSwarm();

    const performanceHistory: number[] = [];
    const diversityHistory: number[] = [];
    let converged = false;
    let convergenceStep = 0;

    for (let step = 0; step < this.config.explorationSteps; step++) {
      // Update each agent
      await Promise.all(this.swarm.map(agent => this.updateAgent(agent, step)));

      // Evaluate circuits and update global best
      await this.evaluateSwarm();

      // Learn patterns from high-performing circuits
      if (step % 10 === 0) {
        await this.learnCircuitPatterns();
      }

      // Use OpenRouter for problem decomposition (optional)
      if (this.openai && step % 50 === 0) {
        await this.decomposeWithLLM();
      }

      // Track convergence
      const avgPerformance = this.swarm.reduce((sum, a) => sum + a.bestPerformance, 0) / this.swarm.length;
      const diversity = this.computeSwarmDiversity();

      performanceHistory.push(avgPerformance);
      diversityHistory.push(diversity);

      // Check convergence
      if (diversity < 0.01 && performanceHistory.length > 20) {
        const recentImprovement = Math.abs(
          performanceHistory[performanceHistory.length - 1] -
          performanceHistory[performanceHistory.length - 20]
        );

        if (recentImprovement < 1e-6) {
          converged = true;
          convergenceStep = step;
          break;
        }
      }
    }

    // Extract learned patterns
    const learnedPatterns = await this.extractLearnedPatterns();

    const executionTime = Date.now() - startTime;

    return {
      bestCircuit: this.globalBest!,
      bestPerformance: this.globalBest?.performance || 0,
      explorationHistory: this.explorationHistory,
      learnedPatterns,
      convergenceData: {
        performanceHistory,
        diversityHistory,
        converged,
        convergenceStep
      },
      executionTime
    };
  }

  /**
   * Initialize swarm with random circuits
   */
  private initializeSwarm(): void {
    this.swarm = [];

    for (let i = 0; i < this.config.swarmSize; i++) {
      const circuit = this.generateRandomCircuit();

      this.swarm.push({
        id: `agent-${i}`,
        position: circuit,
        velocity: Array(circuit.parameters.length).fill(0).map(() => Math.random() - 0.5),
        bestPosition: circuit,
        bestPerformance: -Infinity
      });
    }
  }

  /**
   * Generate random quantum circuit
   */
  private generateRandomCircuit(): QuantumCircuit {
    const depth = Math.floor(Math.random() * this.config.maxDepth) + 1;
    const gates: Gate[] = [];
    const parameters: number[] = [];

    for (let layer = 0; layer < depth; layer++) {
      // Add parameterized rotation gates
      for (let qubit = 0; qubit < this.config.numQubits; qubit++) {
        const gateType = ['RX', 'RY', 'RZ'][Math.floor(Math.random() * 3)] as Gate['type'];
        const param = Math.random() * 2 * Math.PI;

        gates.push({
          type: gateType,
          qubits: [qubit],
          parameter: param
        });

        parameters.push(param);
      }

      // Add entangling gates
      for (let qubit = 0; qubit < this.config.numQubits - 1; qubit++) {
        gates.push({
          type: 'CNOT',
          qubits: [qubit, qubit + 1]
        });
      }
    }

    const metadata = this.computeCircuitMetadata(gates);

    return {
      gates,
      depth,
      numQubits: this.config.numQubits,
      parameters,
      performance: 0,
      metadata
    };
  }

  /**
   * Update agent position using PSO-like dynamics with pattern learning
   */
  private async updateAgent(agent: SwarmAgent, step: number): Promise<void> {
    const w = 0.7; // Inertia weight
    const c1 = 1.5; // Cognitive parameter
    const c2 = 1.5; // Social parameter

    // Query similar high-performing circuits from AgentDB
    const similarCircuits = await this.querySimilarCircuits(agent.position);

    // Update velocity
    for (let i = 0; i < agent.velocity.length; i++) {
      const r1 = Math.random();
      const r2 = Math.random();

      const cognitive = c1 * r1 * (agent.bestPosition.parameters[i] - agent.position.parameters[i]);
      const social = c2 * r2 * (this.globalBest?.parameters[i] || 0 - agent.position.parameters[i]);

      // Add pattern-based attraction
      const patternInfluence = similarCircuits.length > 0
        ? 0.5 * (similarCircuits[0].parameters[i] - agent.position.parameters[i])
        : 0;

      agent.velocity[i] = w * agent.velocity[i] + cognitive + social + patternInfluence;

      // Clamp velocity
      agent.velocity[i] = Math.max(-Math.PI, Math.min(Math.PI, agent.velocity[i]));
    }

    // Update position (circuit parameters)
    const newParameters = agent.position.parameters.map((p, i) => {
      const newP = p + agent.velocity[i];
      return ((newP % (2 * Math.PI)) + 2 * Math.PI) % (2 * Math.PI);
    });

    agent.position = {
      ...agent.position,
      parameters: newParameters
    };
  }

  /**
   * Evaluate swarm and update best circuits
   */
  private async evaluateSwarm(): Promise<void> {
    for (const agent of this.swarm) {
      const performance = await this.evaluateCircuit(agent.position);
      agent.position.performance = performance;

      // Update personal best
      if (performance > agent.bestPerformance) {
        agent.bestPerformance = performance;
        agent.bestPosition = { ...agent.position };
      }

      // Update global best
      if (!this.globalBest || performance > this.globalBest.performance) {
        this.globalBest = { ...agent.position };
      }

      // Store in exploration history
      this.explorationHistory.push({ ...agent.position });
    }
  }

  /**
   * Evaluate circuit performance (problem-specific)
   */
  private async evaluateCircuit(circuit: QuantumCircuit): Promise<number> {
    // Evaluate based on problem type
    switch (this.config.problemType) {
      case 'maxcut':
        return this.evaluateMaxCutCircuit(circuit);
      case 'vqe':
        return this.evaluateVQECircuit(circuit);
      case 'qaoa':
        return this.evaluateQAOACircuit(circuit);
      default:
        return this.evaluateGenericCircuit(circuit);
    }
  }

  /**
   * Evaluate circuit for MaxCut problem
   */
  private evaluateMaxCutCircuit(circuit: QuantumCircuit): number {
    // Simulate circuit and compute MaxCut expectation value
    // Higher is better (more edges cut)

    const { expressibility, entanglingCapability } = circuit.metadata;
    const depthPenalty = circuit.depth / this.config.maxDepth;

    // Combine factors (simplified evaluation)
    return expressibility * entanglingCapability * (1 - 0.3 * depthPenalty);
  }

  /**
   * Evaluate circuit for VQE problem
   */
  private evaluateVQECircuit(circuit: QuantumCircuit): number {
    // Lower energy is better for VQE
    const { expressibility } = circuit.metadata;
    return expressibility;
  }

  /**
   * Evaluate circuit for QAOA problem
   */
  private evaluateQAOACircuit(circuit: QuantumCircuit): number {
    const { expressibility, entanglingCapability } = circuit.metadata;
    return (expressibility + entanglingCapability) / 2;
  }

  /**
   * Generic circuit evaluation
   */
  private evaluateGenericCircuit(circuit: QuantumCircuit): number {
    const { expressibility, entanglingCapability, twoQubitGateCount } = circuit.metadata;
    const efficiency = 1 / (1 + twoQubitGateCount / 10);
    return (expressibility + entanglingCapability) / 2 * efficiency;
  }

  /**
   * Compute circuit metadata
   */
  private computeCircuitMetadata(gates: Gate[]): CircuitMetadata {
    const twoQubitGateCount = gates.filter(g => g.qubits.length > 1).length;

    // Expressibility: measure of state space coverage
    const parameterizedGates = gates.filter(g => g.parameter !== undefined).length;
    const expressibility = Math.min(1, parameterizedGates / (this.config.numQubits * 3));

    // Entangling capability: measure of entanglement generation
    const entanglingCapability = Math.min(1, twoQubitGateCount / (this.config.numQubits - 1));

    return {
      ansatzType: 'hardware-efficient',
      problemType: this.config.problemType,
      twoQubitGateCount,
      expressibility,
      entanglingCapability,
      circuitId: this.generateCircuitId(gates)
    };
  }

  /**
   * Generate unique circuit ID
   */
  private generateCircuitId(gates: Gate[]): string {
    const gateString = gates.map(g => `${g.type}${g.qubits.join(',')}`).join('-');
    return Buffer.from(gateString).toString('base64').substring(0, 16);
  }

  /**
   * Learn patterns from high-performing circuits
   */
  private async learnCircuitPatterns(): Promise<void> {
    // Get top 20% circuits by performance
    const sortedCircuits = [...this.explorationHistory]
      .sort((a, b) => b.performance - a.performance)
      .slice(0, Math.floor(this.explorationHistory.length * 0.2));

    // Store in AgentDB with embeddings
    for (const circuit of sortedCircuits) {
      const embedding = this.circuitToEmbedding(circuit);

      await this.agentDB.store({
        id: circuit.metadata.circuitId,
        embedding,
        metadata: {
          performance: circuit.performance,
          depth: circuit.depth,
          gates: circuit.gates,
          parameters: circuit.parameters,
          ...circuit.metadata
        }
      });

      // Train learning algorithm
      await this.agentDB.learn({
        state: embedding,
        action: circuit.parameters,
        reward: circuit.performance
      });
    }
  }

  /**
   * Convert circuit to embedding vector
   */
  private circuitToEmbedding(circuit: QuantumCircuit): number[] {
    const embedding = Array(128).fill(0);

    // Encode gate sequence
    circuit.gates.forEach((gate, idx) => {
      if (idx < 30) {
        const gateCode = this.gateTypeToCode(gate.type);
        embedding[idx * 4] = gateCode;
        embedding[idx * 4 + 1] = gate.qubits[0] / this.config.numQubits;
        embedding[idx * 4 + 2] = gate.qubits[1] !== undefined ? gate.qubits[1] / this.config.numQubits : 0;
        embedding[idx * 4 + 3] = gate.parameter || 0;
      }
    });

    // Encode circuit properties
    embedding[120] = circuit.depth / this.config.maxDepth;
    embedding[121] = circuit.metadata.expressibility;
    embedding[122] = circuit.metadata.entanglingCapability;
    embedding[123] = circuit.metadata.twoQubitGateCount / 20;
    embedding[124] = circuit.performance;

    return embedding;
  }

  /**
   * Query similar circuits from AgentDB
   */
  private async querySimilarCircuits(circuit: QuantumCircuit): Promise<QuantumCircuit[]> {
    const embedding = this.circuitToEmbedding(circuit);

    const results = await this.agentDB.query({
      embedding,
      topK: 5,
      threshold: 0.7
    });

    return results.map((r: any) => ({
      gates: r.metadata.gates,
      depth: r.metadata.depth,
      numQubits: this.config.numQubits,
      parameters: r.metadata.parameters,
      performance: r.metadata.performance,
      metadata: {
        ansatzType: r.metadata.ansatzType,
        problemType: r.metadata.problemType,
        twoQubitGateCount: r.metadata.twoQubitGateCount,
        expressibility: r.metadata.expressibility,
        entanglingCapability: r.metadata.entanglingCapability,
        circuitId: r.metadata.circuitId
      }
    }));
  }

  /**
   * Extract learned patterns from AgentDB
   */
  private async extractLearnedPatterns(): Promise<CircuitPattern[]> {
    // Get all stored circuits
    const allCircuits = await this.agentDB.getAll();

    // Extract common gate patterns
    const patternMap = new Map<string, { count: number; totalPerf: number; embeddings: number[][] }>();

    allCircuits.forEach((item: any) => {
      const gates = item.metadata.gates as Gate[];
      const pattern = this.extractGatePattern(gates);

      if (!patternMap.has(pattern)) {
        patternMap.set(pattern, { count: 0, totalPerf: 0, embeddings: [] });
      }

      const entry = patternMap.get(pattern)!;
      entry.count++;
      entry.totalPerf += item.metadata.performance;
      entry.embeddings.push(item.embedding);
    });

    // Convert to pattern array
    return Array.from(patternMap.entries()).map(([pattern, data]) => ({
      pattern,
      frequency: data.count,
      averagePerformance: data.totalPerf / data.count,
      embedding: this.averageEmbeddings(data.embeddings)
    }));
  }

  /**
   * Extract gate pattern signature
   */
  private extractGatePattern(gates: Gate[]): string {
    // Extract pattern of first few gates
    return gates.slice(0, 10).map(g => g.type).join('-');
  }

  /**
   * Compute average embedding
   */
  private averageEmbeddings(embeddings: number[][]): number[] {
    if (embeddings.length === 0) return Array(128).fill(0);

    const sum = Array(128).fill(0);
    embeddings.forEach(emb => {
      emb.forEach((val, idx) => {
        sum[idx] += val;
      });
    });

    return sum.map(s => s / embeddings.length);
  }

  /**
   * Use LLM for problem decomposition
   */
  private async decomposeWithLLM(): Promise<void> {
    if (!this.openai) return;

    try {
      const response = await this.openai.chat.completions.create({
        model: 'anthropic/claude-3.5-sonnet',
        messages: [{
          role: 'user',
          content: `Suggest optimal quantum circuit structure for ${this.config.problemType} problem with ${this.config.numQubits} qubits. Focus on gate types and entanglement patterns.`
        }],
        max_tokens: 500
      });

      const suggestion = response.choices[0].message.content;
      console.log('LLM Suggestion:', suggestion);

      // Parse suggestion and influence swarm (simplified)
      // In production, this would parse structured output and modify circuit generation
    } catch (error) {
      console.warn('LLM decomposition failed:', error);
    }
  }

  /**
   * Compute swarm diversity
   */
  private computeSwarmDiversity(): number {
    if (this.swarm.length < 2) return 1;

    let totalDistance = 0;
    let count = 0;

    for (let i = 0; i < this.swarm.length; i++) {
      for (let j = i + 1; j < this.swarm.length; j++) {
        const distance = this.circuitDistance(
          this.swarm[i].position,
          this.swarm[j].position
        );
        totalDistance += distance;
        count++;
      }
    }

    return count > 0 ? totalDistance / count : 0;
  }

  /**
   * Compute distance between circuits
   */
  private circuitDistance(c1: QuantumCircuit, c2: QuantumCircuit): number {
    const paramDist = Math.sqrt(
      c1.parameters.reduce((sum, p, i) => sum + Math.pow(p - c2.parameters[i], 2), 0)
    );
    return paramDist / Math.sqrt(c1.parameters.length);
  }

  /**
   * Convert gate type to numerical code
   */
  private gateTypeToCode(type: Gate['type']): number {
    const codes: Record<Gate['type'], number> = {
      'RX': 0.1, 'RY': 0.2, 'RZ': 0.3,
      'CNOT': 0.4, 'CZ': 0.5,
      'H': 0.6, 'T': 0.7, 'S': 0.8
    };
    return codes[type] || 0;
  }
}

/**
 * Explore circuits with swarm intelligence
 */
export async function exploreCircuits(
  config: Partial<CircuitExplorationConfig>
): Promise<ExplorationResult> {
  const fullConfig: CircuitExplorationConfig = {
    numQubits: config.numQubits || 4,
    problemType: config.problemType || 'maxcut',
    swarmSize: config.swarmSize || 20,
    maxDepth: config.maxDepth || 5,
    explorationSteps: config.explorationSteps || 100,
    learningRate: config.learningRate || 0.01,
    memorySize: config.memorySize || 1000,
    useOpenRouter: config.useOpenRouter,
    openrouterApiKey: config.openrouterApiKey
  };

  const explorer = new SwarmCircuitExplorer(fullConfig);
  return explorer.explore();
}