Quantum Search Architecture

Revolutionary indexing and search system inspired by quantum computing principles, delivering unprecedented performance for enterprise-scale workspaces.

Status: Beta - Available in v1.3, full rollout in v1.4

What is Quantum Architecture?

Lokus Quantum is not actual quantum computing, but a revolutionary indexing and search system inspired by quantum computing principles:

Note: Quantum-Inspired Principles:

Superposition Index: Multiple index states exist simultaneously
Entanglement Linking: Related data automatically cross-references
Probabilistic Querying: Results ranked by quantum-like probability waves
Interference Patterns: Search patterns interfere constructively/destructively
Quantum Annealing: Index optimization through simulated annealing

Why “Quantum”?

The architecture borrows conceptual frameworks from quantum mechanics:

Superposition - Multiple index structures coexist, each optimized for different query types
Entanglement - Related documents are linked across multiple dimensions
Interference - Search terms create wave patterns that amplify relevant results
Collapse - Query execution “collapses” the index to specific results
Annealing - Continuous optimization finds global performance maxima

Architecture Components

1. Quantum Superposition Index (QSI)

The QSI maintains multiple overlapping index structures simultaneously:

interface QuantumSuperpositionIndex {
  // Primary hash-based index - O(1) lookups
  hashIndex: Map<string, QuantumState>
 
  // Semantic embedding index - similarity search
  embeddingIndex: VectorIndex<768> // 768-dim embeddings
 
  // Temporal index - time-based queries
  temporalIndex: TimeSeriesIndex
 
  // Property index - structured data
  propertyIndex: TrieIndex<PropertyValue>
 
  // Graph index - relationship queries
  graphIndex: AdjacencyMatrix
}
 
interface QuantumState {
  fileId: string
  waveFunction: Float32Array // Probability distribution
  phase: number // Query relevance phase
  entanglements: Set<string> // Related entities
  lastCollapse: timestamp // Last accessed
}

How it Works

1. Indexing Phase: When a file is indexed, it’s represented as a quantum state with multiple “superposed” representations:

async function indexFile(file: File): Promise<QuantumState> {
  const state = {
    fileId: file.id,
    waveFunction: new Float32Array(768),
    phase: 0,
    entanglements: new Set(),
    lastCollapse: Date.now()
  };
 
  // Compute embeddings for semantic search
  state.waveFunction = await computeEmbedding(file.content);
 
  // Find entanglements (related files)
  state.entanglements = await findRelatedFiles(file);
 
  // Update all index structures
  qsi.hashIndex.set(file.id, state);
  qsi.embeddingIndex.insert(state.waveFunction, file.id);
  qsi.temporalIndex.insert(file.modified, file.id);
  qsi.propertyIndex.insert(file.properties, file.id);
 
  return state;
}

2. Query Phase: Searches create interference patterns across all index structures:

async function quantumSearch(query: string): Promise<SearchResult[]> {
  // Create query wave function
  const queryWave = await computeEmbedding(query);
 
  // Generate interference patterns across indices
  const hashResults = qsi.hashIndex.get(query);
  const embeddingResults = qsi.embeddingIndex.search(queryWave, 100);
  const temporalResults = qsi.temporalIndex.getRecent(100);
 
  // Constructive/destructive interference
  const interference = computeInterference([
    hashResults,
    embeddingResults,
    temporalResults
  ]);
 
  return interference;
}

3. Collapse Phase: Results “collapse” to the most relevant matches based on constructive interference:

function computeInterference(results: SearchResult[][]): SearchResult[] {
  const scoreMap = new Map<string, number>();
 
  // Combine scores with wave interference
  for (const resultSet of results) {
    for (const result of resultSet) {
      const currentScore = scoreMap.get(result.id) || 0;
      const waveAmplitude = Math.cos(result.phase) * result.probability;
      scoreMap.set(result.id, currentScore + waveAmplitude);
    }
  }
 
  // Sort by interference amplitude
  return Array.from(scoreMap.entries())
    .map(([id, score]) => ({ id, score }))
    .sort((a, b) => b.score - a.score);
}

4. Caching: Collapsed states are cached for instant re-access:

class CollapseCache {
  private cache = new LRUCache<string, SearchResult[]>(1000);
 
  collapse(query: string, results: SearchResult[]): void {
    this.cache.set(query, results);
  }
 
  retrieve(query: string): SearchResult[] | null {
    return this.cache.get(query);
  }
}

2. Neural Semantic Cache

AI-powered predictive caching system that learns user behavior:

class NeuralSemanticCache {
  private model: TransformerModel // Lightweight BERT-like model
  private cache: LRUCache<string, CachedResult>
  private accessPatterns: AccessPattern[]
 
  async predictNextQuery(currentQuery: string): Promise<string[]> {
    // Analyze query patterns
    const embedding = await this.model.encode(currentQuery);
    const predictions = this.model.predictNext(embedding);
 
    // Pre-fetch likely next queries
    return predictions.map(p => p.query);
  }
 
  async prefetch(queries: string[]): Promise<void> {
    // Background prefetching
    for (const query of queries) {
      if (!this.cache.has(query)) {
        this.executeQuery(query).then(result => {
          this.cache.set(query, result);
        });
      }
    }
  }
 
  learn(query: string, results: SearchResult[]): void {
    // Update model with new patterns
    this.accessPatterns.push({
      query,
      timestamp: Date.now(),
      results: results.length
    });
 
    // Retrain periodically
    if (this.accessPatterns.length % 100 === 0) {
      this.retrain();
    }
  }
}

Features:

Learns from user query patterns
Predicts next likely searches with 80%+ accuracy
Pre-fetches results in background
80%+ cache hit rate after warmup period
Continuous learning from user behavior

Cache Hit Rate Over Time:

Time Period	Cache Hit Rate	Avg Query Time
First 10 queries	20%	35ms
10-50 queries	45%	18ms
50-200 queries	68%	8ms
200+ queries	83%	3ms

3. Stream Processing Pipeline

Event-sourced reactive data flow for real-time indexing:

class StreamProcessor {
  private eventStream: Observable<FileEvent>
  private indexUpdater: Subject<IndexUpdate>
  private queryEngine: QueryEngine
 
  constructor() {
    // File events → Index updates → Query results
    this.eventStream
      .pipe(
        debounceTime(50), // Batch updates
        bufferCount(100), // Process in batches
        mergeMap(events => this.processEvents(events)),
        tap(updates => this.updateIndex(updates))
      )
      .subscribe();
  }
 
  private async processEvents(events: FileEvent[]): Promise<IndexUpdate[]> {
    // Incremental index updates
    return events.map(event => ({
      type: event.type,
      fileId: event.fileId,
      delta: this.computeDelta(event)
    }));
  }
 
  private computeDelta(event: FileEvent): IndexDelta {
    // Only update changed portions
    switch (event.type) {
      case 'content':
        return { embeddings: true, hash: true };
      case 'metadata':
        return { properties: true, temporal: true };
      case 'links':
        return { graph: true, entanglements: true };
      default:
        return { hash: true };
    }
  }
}

Benefits:

Zero UI blocking during indexing
Incremental updates only (no full re-indexing)
Real-time search results as you type
Memory-efficient batching (100 events/batch)
50ms debouncing prevents thrashing

4. Hierarchical Temporal Memory (HTM)

Pattern learning system inspired by neuroscience:

interface TemporalMemory {
  // Learn patterns from access history
  learn(pattern: AccessPattern): void
 
  // Predict likely future accesses
  predict(context: QueryContext): Prediction[]
 
  // Anomaly detection
  detectAnomalies(pattern: AccessPattern): Anomaly[]
}
 
class HTMIndexer implements TemporalMemory {
  private corticalColumns: CorticalColumn[]
  private spatialPooler: SpatialPooler
  private temporalMemory: TemporalMemory
 
  learn(pattern: AccessPattern): void {
    // Spatial pooling - recognize patterns
    const activeColumns = this.spatialPooler.compute(pattern);
 
    // Temporal memory - predict sequences
    this.temporalMemory.compute(activeColumns, true);
  }
 
  predict(context: QueryContext): Prediction[] {
    const activeColumns = this.spatialPooler.compute(context);
    const predictions = this.temporalMemory.getPredictiveCells();
 
    return predictions.map(cell => ({
      fileId: this.cellToFile(cell),
      confidence: cell.confidence,
      latency: cell.expectedTime
    }));
  }
 
  detectAnomalies(pattern: AccessPattern): Anomaly[] {
    const expected = this.predict(pattern.context);
    const actual = pattern.results;
 
    // High divergence indicates anomaly
    if (this.divergence(expected, actual) > 0.7) {
      return [{
        type: 'unexpected_pattern',
        severity: 'high',
        details: 'User behavior significantly different from learned patterns'
      }];
    }
 
    return [];
  }
}

Applications:

Predictive Prefetching: Load files before user requests them
Smart Cache Eviction: Keep likely-accessed files in cache
Query Optimization: Reorder operations based on predicted access
Anomaly Detection: Identify corrupted files, unusual patterns, potential security issues

5. WebAssembly Compute Engine

Near-native performance for critical operations:

// Rust WASM module for hot-path operations
#[wasm_bindgen]
pub struct QuantumIndexer {
    index: HashMap<String, QuantumState>,
    embeddings: Vec<f32>,
}
 
#[wasm_bindgen]
impl QuantumIndexer {
    pub fn search(&self, query: &str, limit: usize) -> Vec<SearchResult> {
        // High-performance search in Rust
        let query_embedding = self.embed(query);
        let mut results = Vec::new();
 
        for (id, state) in &self.index {
            let score = cosine_similarity(&query_embedding, &state.embedding);
            if score > 0.7 {
                results.push(SearchResult { id: id.clone(), score });
            }
        }
 
        results.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap());
        results.truncate(limit);
        results
    }
 
    // Vectorized operations for massive speedups
    fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
        // SIMD-optimized dot product
        a.iter().zip(b.iter()).map(|(x, y)| x * y).sum::<f32>()
            / (norm(a) * norm(b))
    }
 
    // Batch processing for efficiency
    pub fn batch_search(&self, queries: Vec<String>, limit: usize) -> Vec<Vec<SearchResult>> {
        queries.par_iter()
            .map(|q| self.search(q, limit))
            .collect()
    }
}

Performance Benefits:

Operation	JavaScript	WASM (Rust)	Speedup
Cosine similarity	2.5ms	0.05ms	50x
Embedding search (10K vectors)	450ms	12ms	37.5x
Batch search (100 queries)	3800ms	150ms	25x

Technical Advantages:

10-50x faster than JavaScript for compute-intensive operations
SIMD vectorization for parallel processing
Zero garbage collection overhead
Direct memory access
Parallel processing with Rayon

Enabling Quantum Architecture

Note: Beta Feature: Quantum architecture is in beta. Enable with caution on production workspaces. We recommend testing on a copy of your workspace first.

Via Settings UI

Navigate to: Preferences → Performance → Quantum Search

{
  "performance": {
    "quantumSearch": {
      "enabled": true,
      "indexType": "full", // "full" | "hybrid" | "fallback"
      "semanticCache": true,
      "predictivePrefetch": true,
      "wasmAcceleration": true
    }
  }
}

Index Type Options:

full: Complete Quantum index (highest performance, more memory)
hybrid: Quantum + traditional fallback (balanced)
fallback: Automatic fallback to traditional for unsupported queries

Via API

import { quantumIndexer } from '@lokus/quantum'
 
// Initialize Quantum indexer
const indexer = await quantumIndexer.initialize({
  workspacePath: '/path/to/workspace',
  indexType: 'full',
  cacheSize: 1000, // MB
  embeddingModel: 'lightweight' // or 'standard', 'high-quality'
})
 
// Perform quantum-powered search
const results = await indexer.search({
  query: 'machine learning algorithms',
  filters: {
    tags: ['ai', 'research'],
    dateRange: { start: '2024-01-01', end: '2024-12-31' }
  },
  semanticSearch: true,
  limit: 20
})
 
// Results include quantum relevance scores
results.forEach(result => {
  console.log(`${result.file}: ${result.quantumScore}`)
})

Advanced Configuration

// Fine-tune Quantum parameters
await indexer.configure({
  // Neural cache settings
  neuralCache: {
    maxSize: 1000,
    learningRate: 0.01,
    predictionThreshold: 0.7
  },
 
  // HTM settings
  htm: {
    corticalColumns: 2048,
    activationThreshold: 0.5,
    learningEnabled: true
  },
 
  // Stream processing
  streaming: {
    debounceMs: 50,
    batchSize: 100,
    maxConcurrent: 4
  },
 
  // WASM settings
  wasm: {
    threadCount: 4,
    simdEnabled: true,
    memoryLimit: 512 // MB
  }
})

Performance Comparison

Real-World Benchmarks

10,000 File Workspace:

Query Type	Standard	Quantum	Improvement
Simple keyword	2,400ms	22ms	109x faster
Multi-term search	3,200ms	28ms	114x faster
Semantic search	8,500ms	85ms	100x faster
Filtered search	4,100ms	35ms	117x faster
Graph traversal	1,800ms	18ms	100x faster

50,000 File Workspace:

Query Type	Standard	Quantum	Improvement
Simple keyword	15,000ms	85ms	176x faster
Multi-term search	22,000ms	110ms	200x faster
Semantic search	45,000ms	320ms	140x faster

Best Practices

Note: Quantum Optimization Tips:

Enable All Features: Use full Quantum stack for best performance
Warm Up Cache: First 50-100 queries build the neural cache
Use Semantic Search: Leverage embeddings for better results
Batch Operations: Group related queries together
Monitor Memory: Watch cache sizes on large workspaces
Regular Reindexing: Full reindex weekly for optimal performance

Limitations & Considerations

Current Limitations

Beta stability: May have edge cases
Memory overhead: Requires 100-200MB additional RAM
Initial indexing: First index takes 2-3x longer
WASM requirement: Needs WebAssembly support

When to Use

Great for:

Large workspaces (5,000+ files)
Frequent searches
Semantic/similarity queries
Complex filters
Real-time results

Not needed for:

Small workspaces (<1,000 files)
Rare searches
Simple exact matches
Memory-constrained systems

Troubleshooting

High Memory Usage

{
  "quantumSearch": {
    "cacheSize": 500,
    "embeddingModel": "lightweight"
  }
}

Slow Initial Indexing

// Index in background on app start
await indexer.initialize({
  background: true,
  priority: 'low'
})

WASM Not Loading

Check console for errors, ensure WASM is enabled in browser settings.

Next Steps

Optimization Techniques - Detailed tuning guides
Performance Overview - Benchmarks and specs
Configuration Reference - All settings explained

Overview Optimization