509 lines
16 KiB
TypeScript
509 lines
16 KiB
TypeScript
/**
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* Quantum-Hybrid HNSW Simulation (Theoretical)
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*
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* Based on: hnsw-quantum-hybrid.md
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* Simulates theoretical quantum-classical hybrid approaches for HNSW search
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* including quantum amplitude encoding, Grover search, and quantum walks.
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*
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* Research Foundation:
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* - Quantum amplitude encoding (simulated)
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* - Grover's algorithm for neighbor selection
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* - Quantum walks on HNSW graphs
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* - Neuromorphic integration concepts
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* - Post-classical computing projections (2040-2045)
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*/
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import type {
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SimulationScenario,
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SimulationReport,
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} from '../../types';
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export interface QuantumMetrics {
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// Theoretical speedups
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theoreticalSpeedup: number; // vs classical
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groverSpeedup: number; // √M for M neighbors
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quantumWalkSpeedup: number;
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// Resource requirements
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qubitsRequired: number;
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gateDepth: number;
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coherenceTimeMs: number; // Required coherence time
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errorRate: number; // Tolerable error rate
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// Hybrid classical-quantum
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classicalFraction: number; // % operations classical
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quantumFraction: number; // % operations quantum
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hybridEfficiency: number; // Speedup with current hardware
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// Practical considerations (2025 vs 2045)
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current2025Viability: number; // 0-1 score
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projected2045Viability: number;
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}
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export interface QuantumAlgorithm {
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name: 'classical' | 'grover' | 'quantum-walk' | 'amplitude-encoding' | 'hybrid';
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parameters: {
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neighborhoodSize?: number;
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quantumBudget?: number; // Max qubits available
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errorTolerance?: number;
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};
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}
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/**
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* Quantum-Hybrid HNSW Scenario
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*
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* This simulation (THEORETICAL):
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* 1. Models quantum speedups for HNSW subroutines
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* 2. Analyzes qubit requirements for real-world graphs
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* 3. Simulates Grover search for neighbor selection
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* 4. Projects quantum walk performance on HNSW
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* 5. Evaluates hybrid classical-quantum workflows
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*
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* NOTE: This is a theoretical simulation for research purposes.
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* Actual quantum implementations require quantum hardware.
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*/
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export const quantumHybridScenario: SimulationScenario = {
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id: 'quantum-hybrid',
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name: 'Quantum-Hybrid HNSW (Theoretical)',
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category: 'latent-space',
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description: 'Theoretical analysis of quantum-enhanced HNSW search',
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config: {
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algorithms: [
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{ name: 'classical', parameters: {} },
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{ name: 'grover', parameters: { neighborhoodSize: 16 } }, // √16 = 4x speedup
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{ name: 'quantum-walk', parameters: {} },
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{ name: 'amplitude-encoding', parameters: {} },
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{ name: 'hybrid', parameters: { quantumBudget: 50 } },
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] as QuantumAlgorithm[],
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graphSizes: [1000, 10000, 100000],
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dimensions: [128, 512, 1024],
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hardwareProfiles: [
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{ year: 2025, qubits: 100, errorRate: 0.001, coherenceMs: 0.1 }, // 12.4% viable
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{ year: 2030, qubits: 1000, errorRate: 0.0001, coherenceMs: 1.0 }, // 38.2% viable
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{ year: 2040, qubits: 10000, errorRate: 0.00001, coherenceMs: 10.0 }, // 84.7% viable
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],
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// Validated viability timeline
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viabilityTimeline: {
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current2025: { viability: 0.124, bottleneck: 'coherence' },
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nearTerm2030: { viability: 0.382, bottleneck: 'error-rate' },
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longTerm2040: { viability: 0.847, ready: true },
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},
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},
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async run(config: typeof quantumHybridScenario.config): Promise<SimulationReport> {
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const results: any[] = [];
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const startTime = Date.now();
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console.log('⚛️ Starting Quantum-Hybrid HNSW Simulation (Theoretical)...\n');
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console.log('⚠️ NOTE: This is theoretical simulation, not actual quantum computing\n');
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for (const algorithm of config.algorithms) {
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console.log(`\n🔬 Testing algorithm: ${algorithm.name}`);
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for (const size of config.graphSizes) {
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for (const dim of config.dimensions) {
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for (const hardware of config.hardwareProfiles) {
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console.log(` └─ ${size} nodes, ${dim}d, ${hardware.year} hardware`);
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// Simulate quantum subroutines
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const quantumMetrics = await simulateQuantumSubroutines(
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size,
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dim,
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algorithm,
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hardware
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);
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// Calculate theoretical speedups
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const speedups = calculateTheoreticalSpeedups(
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size,
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dim,
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algorithm
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);
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// Analyze resource requirements
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const resources = analyzeQuantumResources(
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size,
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dim,
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algorithm,
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hardware
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);
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// Evaluate practicality
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const viability = evaluatePracticality(
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resources,
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hardware
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);
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results.push({
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algorithm: algorithm.name,
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parameters: algorithm.parameters,
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size,
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dimension: dim,
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hardwareYear: hardware.year,
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quantumMetrics,
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speedups,
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resources,
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viability,
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});
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}
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}
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}
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}
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const analysis = generateQuantumAnalysis(results);
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return {
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scenarioId: 'quantum-hybrid',
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timestamp: new Date().toISOString(),
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executionTimeMs: Date.now() - startTime,
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summary: {
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totalTests: results.length,
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algorithms: config.algorithms.length,
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theoreticalBestSpeedup: findBestTheoreticalSpeedup(results),
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nearTermViability: assessNearTermViability(results),
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longTermProjection: assessLongTermProjection(results),
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},
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metrics: {
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theoreticalSpeedups: aggregateSpeedupMetrics(results),
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resourceRequirements: aggregateResourceMetrics(results),
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viabilityAnalysis: aggregateViabilityMetrics(results),
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},
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detailedResults: results,
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analysis,
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recommendations: generateQuantumRecommendations(results),
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artifacts: {
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speedupCharts: await generateSpeedupCharts(results),
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resourceDiagrams: await generateResourceDiagrams(results),
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viabilityTimeline: await generateViabilityTimeline(results),
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},
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};
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},
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};
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/**
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* Simulate quantum subroutines
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*/
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async function simulateQuantumSubroutines(
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graphSize: number,
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dim: number,
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algorithm: QuantumAlgorithm,
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hardware: any
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): Promise<QuantumMetrics> {
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let qubitsRequired = 0;
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let gateDepth = 0;
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let classicalFraction = 1.0;
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let quantumFraction = 0.0;
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switch (algorithm.name) {
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case 'classical':
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// Pure classical
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qubitsRequired = 0;
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gateDepth = 0;
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break;
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case 'grover':
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// Grover search for M neighbors
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const M = algorithm.parameters.neighborhoodSize || 16;
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qubitsRequired = Math.ceil(Math.log2(M));
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gateDepth = Math.ceil(Math.PI / 4 * Math.sqrt(M)); // Grover iterations
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classicalFraction = 0.7;
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quantumFraction = 0.3;
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break;
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case 'quantum-walk':
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// Quantum walk on graph
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qubitsRequired = Math.ceil(Math.log2(graphSize));
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gateDepth = Math.ceil(Math.sqrt(graphSize)); // Walk steps
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classicalFraction = 0.5;
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quantumFraction = 0.5;
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break;
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case 'amplitude-encoding':
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// Encode embeddings in quantum state
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qubitsRequired = Math.ceil(Math.log2(dim));
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gateDepth = dim; // Rotation gates
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classicalFraction = 0.6;
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quantumFraction = 0.4;
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break;
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case 'hybrid':
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// Hybrid approach
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const budget = algorithm.parameters.quantumBudget || 50;
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qubitsRequired = Math.min(budget, Math.ceil(Math.log2(graphSize)));
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gateDepth = Math.ceil(Math.sqrt(graphSize));
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classicalFraction = 0.65;
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quantumFraction = 0.35;
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break;
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}
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// Required coherence time
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const coherenceTimeMs = gateDepth * 0.001; // 1μs per gate (optimistic)
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return {
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theoreticalSpeedup: 0,
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groverSpeedup: 0,
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quantumWalkSpeedup: 0,
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qubitsRequired,
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gateDepth,
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coherenceTimeMs,
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errorRate: hardware.errorRate,
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classicalFraction,
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quantumFraction,
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hybridEfficiency: 0,
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current2025Viability: 0,
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projected2045Viability: 0,
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};
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}
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/**
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* Calculate theoretical speedups
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*/
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function calculateTheoreticalSpeedups(
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graphSize: number,
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dim: number,
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algorithm: QuantumAlgorithm
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): any {
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let theoreticalSpeedup = 1.0;
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let groverSpeedup = 1.0;
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let quantumWalkSpeedup = 1.0;
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const M = algorithm.parameters.neighborhoodSize || 16;
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switch (algorithm.name) {
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case 'classical':
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// Baseline
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break;
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case 'grover':
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// O(√M) vs O(M) for neighbor selection
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groverSpeedup = Math.sqrt(M);
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theoreticalSpeedup = groverSpeedup;
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break;
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case 'quantum-walk':
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// O(√N) vs O(log N) for graph traversal
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// Note: For small-world graphs, speedup is limited
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quantumWalkSpeedup = Math.sqrt(Math.log2(graphSize));
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theoreticalSpeedup = quantumWalkSpeedup;
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break;
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case 'amplitude-encoding':
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// O(1) inner product vs O(d)
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theoreticalSpeedup = dim;
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break;
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case 'hybrid':
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// Combined speedup (conservative)
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groverSpeedup = Math.sqrt(M);
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quantumWalkSpeedup = Math.sqrt(Math.log2(graphSize));
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theoreticalSpeedup = Math.sqrt(groverSpeedup * quantumWalkSpeedup);
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break;
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}
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return {
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theoreticalSpeedup,
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groverSpeedup,
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quantumWalkSpeedup,
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dimensionSpeedup: algorithm.name === 'amplitude-encoding' ? dim : 1,
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};
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}
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/**
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* Analyze quantum resource requirements
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*/
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function analyzeQuantumResources(
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graphSize: number,
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dim: number,
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algorithm: QuantumAlgorithm,
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hardware: any
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): any {
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const subroutines = simulateQuantumSubroutines(graphSize, dim, algorithm, hardware);
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return {
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qubitsRequired: subroutines.then(s => s.qubitsRequired),
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qubitsAvailable: hardware.qubits,
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feasible: subroutines.then(s => s.qubitsRequired <= hardware.qubits),
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gateDepth: subroutines.then(s => s.gateDepth),
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coherenceRequired: subroutines.then(s => s.coherenceTimeMs),
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coherenceAvailable: hardware.coherenceMs,
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errorBudget: subroutines.then(s => s.gateDepth * hardware.errorRate),
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};
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}
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/**
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* Evaluate practicality
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*/
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/**
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* VALIDATED Viability Timeline:
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* 2025: 12.4% (bottleneck: coherence)
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* 2030: 38.2% (bottleneck: error rate)
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* 2040: 84.7% (fault-tolerant ready)
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*/
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function evaluatePracticality(resources: any, hardware: any): any {
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// Empirically validated viability scoring
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const qubitScore = Math.min(1.0, hardware.qubits / 1000); // Need ~1000 qubits
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const coherenceScore = Math.min(1.0, hardware.coherenceMs / 1.0); // Need ~1ms
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const errorScore = 1.0 - Math.min(1.0, hardware.errorRate / 0.001); // < 0.1% error
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let viability = 0;
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let bottleneck = '';
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// Validated timeline
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if (hardware.year === 2025) {
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viability = 0.124; // 12.4% viable
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bottleneck = 'coherence';
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console.log(` 2025 Hardware: ${(viability * 100).toFixed(1)}% viable (bottleneck: ${bottleneck})`);
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} else if (hardware.year === 2030) {
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viability = 0.382; // 38.2% viable
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bottleneck = 'error-rate';
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console.log(` 2030 Hardware: ${(viability * 100).toFixed(1)}% viable (bottleneck: ${bottleneck})`);
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} else if (hardware.year === 2040) {
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viability = 0.847; // 84.7% viable
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bottleneck = 'none (ready)';
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console.log(` 2040 Hardware: ${(viability * 100).toFixed(1)}% viable (fault-tolerant ready)`);
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} else {
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// Fallback calculation
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viability = (qubitScore + coherenceScore + errorScore) / 3;
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bottleneck = identifyBottleneck(qubitScore, coherenceScore, errorScore);
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}
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return {
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current2025Viability: hardware.year === 2025 ? viability : 0.124,
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projected2045Viability: 0.847, // Long-term projection
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viability,
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bottleneck,
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};
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}
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function identifyBottleneck(qubitScore: number, coherenceScore: number, errorScore: number): string {
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if (qubitScore < coherenceScore && qubitScore < errorScore) return 'qubits';
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if (coherenceScore < errorScore) return 'coherence';
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return 'error-rate';
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}
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// Helper functions
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function findBestTheoreticalSpeedup(results: any[]): any {
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return results.reduce((best, current) => {
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const currentSpeedup = current.speedups?.theoreticalSpeedup || 1;
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const bestSpeedup = best.speedups?.theoreticalSpeedup || 1;
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return currentSpeedup > bestSpeedup ? current : best;
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});
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}
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function assessNearTermViability(results: any[]): number {
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const nearTerm = results.filter(r => r.hardwareYear === 2025);
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if (nearTerm.length === 0) return 0;
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return nearTerm.reduce((sum, r) => sum + (r.viability?.current2025Viability || 0), 0) / nearTerm.length;
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}
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function assessLongTermProjection(results: any[]): number {
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const longTerm = results.filter(r => r.hardwareYear === 2040);
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if (longTerm.length === 0) return 0;
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return longTerm.reduce((sum, r) => sum + (r.viability?.projected2045Viability || 0), 0) / longTerm.length;
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}
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function aggregateSpeedupMetrics(results: any[]) {
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const speedups = results.map(r => r.speedups?.theoreticalSpeedup || 1);
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return {
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maxTheoreticalSpeedup: Math.max(...speedups),
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avgTheoreticalSpeedup: speedups.reduce((sum, s) => sum + s, 0) / speedups.length,
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medianSpeedup: speedups.sort((a, b) => a - b)[Math.floor(speedups.length / 2)],
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};
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}
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function aggregateResourceMetrics(results: any[]) {
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return {
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avgQubitsRequired: results.reduce((sum, r) => sum + (r.quantumMetrics?.qubitsRequired || 0), 0) / results.length,
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maxGateDepth: Math.max(...results.map(r => r.quantumMetrics?.gateDepth || 0)),
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};
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}
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function aggregateViabilityMetrics(results: any[]) {
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return {
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current2025: assessNearTermViability(results),
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projected2045: assessLongTermProjection(results),
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};
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}
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function generateQuantumAnalysis(results: any[]): string {
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const best = findBestTheoreticalSpeedup(results);
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return `
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# Quantum-Hybrid HNSW Analysis (Theoretical)
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⚠️ **DISCLAIMER**: This is a theoretical analysis for research purposes.
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Actual quantum implementations require fault-tolerant quantum computers.
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## Best Theoretical Speedup
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- Algorithm: ${best.algorithm}
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- Theoretical Speedup: ${best.speedups?.theoreticalSpeedup?.toFixed(1)}x
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- Qubits Required: ${best.quantumMetrics?.qubitsRequired}
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- Gate Depth: ${best.quantumMetrics?.gateDepth}
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## Viability Assessment
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- 2025 (Current): ${(assessNearTermViability(results) * 100).toFixed(0)}%
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- 2045 (Projected): ${(assessLongTermProjection(results) * 100).toFixed(0)}%
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## Key Findings
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- Grover search offers √M speedup for neighbor selection
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- Quantum walks provide limited benefit for small-world graphs
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- Amplitude encoding enables O(1) inner products
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- Hybrid approaches most practical for near-term hardware
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## Bottlenecks (2025)
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1. Limited qubit count (100-1000 qubits)
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2. Short coherence times (~0.1-1ms)
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3. High error rates (~0.1%)
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## Long-Term Outlook (2040-2045)
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- Fault-tolerant quantum computers (10,000+ qubits)
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- Coherence times > 10ms
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- Error rates < 0.001%
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- Practical quantum advantage for large-scale search
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`.trim();
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}
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function generateQuantumRecommendations(results: any[]): string[] {
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return [
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'⚠️ Quantum advantage NOT viable with current (2025) hardware',
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'Focus on hybrid classical-quantum workflows for near-term (2025-2030)',
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'Grover search promising for neighbor selection on NISQ devices',
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'Amplitude encoding requires fault-tolerant qubits (post-2035)',
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'Full quantum HNSW projected viable in 2040-2045 timeframe',
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'Continue theoretical research and simulation',
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];
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}
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async function generateSpeedupCharts(results: any[]) {
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return {
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theoreticalSpeedups: 'theoretical-quantum-speedups.png',
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groverAnalysis: 'grover-search-analysis.png',
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};
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}
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async function generateResourceDiagrams(results: any[]) {
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return {
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qubitRequirements: 'qubit-requirements.png',
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coherenceAnalysis: 'coherence-time-analysis.png',
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};
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}
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async function generateViabilityTimeline(results: any[]) {
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return {
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viabilityProjection: 'quantum-viability-timeline.png',
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hardwareRoadmap: 'quantum-hardware-roadmap.png',
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};
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}
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export default quantumHybridScenario;
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