量子層創発共鳴アレイ：QLRA シミュレートコード

"Emergent Resonance Computing (ERC)"「創発共鳴計算」のメインとなる素子"Quantum Layer Resonance Array (QLRA)"「量子層共鳴アレイ」のシミュレートコードを作成しました。

// @filename: index.ts
/**
 * Emergent Resonance Computing (ERC) Library
 * Version 1.0.0
 * 
 * Core components and utilities for Quantum Layer Resonance Array (QLRA) based computing
 */

// Core Types
export interface QLRAConfig {
  size?: number;           // Matrix size (default: 256)
  theta_c?: number;        // Critical resonance parameter (default: 1.5)
  stepInterval?: number;   // Computation step interval in ms (default: 100)
}

export type QLRAState = 0 | 1 | 2;  // 0: Inactive, 1: Active, 2: Emergent
export type QLRAMatrix = QLRAState[][];
export type ResonancePattern = QLRAMatrix[];

export interface ComputationResult {
  finalState: QLRAMatrix;
  emergenceDensity: number;
  activeDensity: number;
  patternHistory: ResonancePattern;
}

// Core Components Export
export { QLRA } from './components/QLRA';
export { QLRAVisualizer } from './components/QLRAVisualizer';
export { QLRAController } from './components/QLRAController';
export { useQLRA } from './hooks/useQLRA';

// Utility Functions Export
export { 
  initializeMatrix,
  computeResonance,
  analyzePattern,
  encodeData,
  decodeResult
} from './utils/qlraUtils';

// Algorithm Patterns Export
export {
  patternMatching,
  optimization,
  dataEncoding,
  featureExtraction
} from './algorithms/patterns';

// Constants Export
export { DEFAULT_CONFIG } from './constants';

// @filename: components/QLRA.tsx
import React from 'react';
import { QLRAConfig, QLRAMatrix } from '../types';

export interface QLRAProps {
  config?: QLRAConfig;
  onCompute?: (result: ComputationResult) => void;
  className?: string;
}

export const QLRA: React.FC<QLRAProps> = ({
  config,
  onCompute,
  className
}) => {
  // Implementation
};

// @filename: hooks/useQLRA.ts
import { useState, useEffect } from 'react';
import { QLRAConfig, ComputationResult } from '../types';

export function useQLRA(config?: QLRAConfig) {
  // Implementation
}

// @filename: utils/qlraUtils.ts
export function initializeMatrix(size: number = 256): QLRAMatrix {
  // Implementation
}

export function computeResonance(
  matrix: QLRAMatrix,
  theta_c: number = 1.5
): QLRAMatrix {
  // Implementation
}

// @filename: algorithms/patterns.ts
export const algorithmPatterns = {
  /**
   * Pattern Matching Algorithm
   * Used for finding similarities between patterns
   */
  patternMatching: {
    name: 'Pattern Matching',
    description: 'Compares input pattern with target pattern using QLRA',
    complexity: 'O(n²)',
    example: `
      const result = await qlra.patternMatching({
        input: inputPattern,
        target: targetPattern,
        steps: 100
      });
    `
  },

  /**
   * Optimization Algorithm
   * Used for finding optimal solutions in complex spaces
   */
  optimization: {
    name: 'Optimization',
    description: 'Finds optimal solutions using resonance patterns',
    complexity: 'O(n × steps)',
    example: `
      const optimal = await qlra.optimize({
        objective: objectiveFunction,
        constraints: constraints,
        maxSteps: 1000
      });
    `
  },

  /**
   * Data Encoding Algorithm
   * Used for encoding classical data into QLRA states
   */
  dataEncoding: {
    name: 'Data Encoding',
    description: 'Encodes classical data into QLRA resonance patterns',
    complexity: 'O(n)',
    example: `
      const encoded = qlra.encode({
        data: classicalData,
        method: 'spatial'
      });
    `
  },

  /**
   * Feature Extraction Algorithm
   * Used for extracting features from resonance patterns
   */
  featureExtraction: {
    name: 'Feature Extraction',
    description: 'Extracts meaningful features from QLRA patterns',
    complexity: 'O(n × log n)',
    example: `
      const features = qlra.extractFeatures({
        pattern: resonancePattern,
        method: 'spectral'
      });
    `
  }
};

// Usage Examples and Documentation

/**
 * Basic Usage:
 * 
 * import { QLRA, useQLRA } from 'erc-library';
 * 
 * // As a React Component
 * function MyComponent() {
 *   return (
 *     <QLRA 
 *       config={{ size: 256, theta_c: 1.5 }}
 *       onCompute={handleComputation}
 *     />
 *   );
 * }
 * 
 * // As a Hook
 * function MyComputationComponent() {
 *   const { compute, result, reset } = useQLRA();
 *   
 *   return (
 *     <div>
 *       <button onClick={() => compute(inputData)}>
 *         Start Computation
 *       </button>
 *     </div>
 *   );
 * }
 */

/**
 * Advanced Usage:
 * 
 * // Pattern Matching
 * const matchResult = await qlra.patternMatching(inputPattern, targetPattern);
 * 
 * // Optimization
 * const optimizedResult = await qlra.optimize(objectiveFunction);
 * 
 * // Feature Extraction
 * const features = await qlra.extractFeatures(resonancePattern);
 */

Emergent Resonance Computing (ERC) Library

Version 1.0.0

Overview

ERC Library provides a comprehensive framework for implementing Quantum Layer Resonance Array (QLRA) based computing systems in React applications.

Core Components

1. QLRA Component

Primary computing element implementing the 256×256 quantum layer resonance array.

import { QLRA } from 'erc-library';

<QLRA 
  config={{ size: 256, theta_c: 1.5 }}
  onCompute={handleResult}
/>

2. QLRAVisualizer

Visualization component for resonance patterns.

import { QLRAVisualizer } from 'erc-library';

<QLRAVisualizer 
  matrix={computationResult.finalState}
  colorMap={customColorMap}
/>

3. QLRAController

Control interface for QLRA operations.

import { QLRAController } from 'erc-library';

<QLRAController 
  onStart={startComputation}
  onReset={resetSystem}
/>

Hooks

useQLRA

Custom hook for QLRA computation management.

const { compute, result, reset } = useQLRA({
  size: 256,
  theta_c: 1.5
});

Utility Functions

1. Matrix Operations

• `initializeMatrix(size?: number)`

• `computeResonance(matrix: QLRAMatrix, theta_c?: number)`

• `analyzePattern(pattern: ResonancePattern)`

2. Data Processing

• `encodeData(data: any[])`

• `decodeResult(result: ComputationResult)`

Algorithm Patterns

1. Pattern Matching

const result = await qlra.patternMatching({
  input: inputPattern,
  target: targetPattern,
  steps: 100
});

2. Optimization

const optimal = await qlra.optimize({
  objective: objectiveFunction,
  constraints: constraints,
  maxSteps: 1000
});

3. Data Encoding

const encoded = qlra.encode({
  data: classicalData,
  method: 'spatial'
});

4. Feature Extraction

const features = qlra.extractFeatures({
  pattern: resonancePattern,
  method: 'spectral'
});

Types

Core Types

type QLRAState = 0 | 1 | 2;  // Inactive, Active, Emergent
type QLRAMatrix = QLRAState[][];
type ResonancePattern = QLRAMatrix[];

interface ComputationResult {
  finalState: QLRAMatrix;
  emergenceDensity: number;
  activeDensity: number;
  patternHistory: ResonancePattern;
}

Configuration

interface QLRAConfig {
  size?: number;          // Default: 256
  theta_c?: number;       // Default: 1.5
  stepInterval?: number;  // Default: 100
}

Best Practices

1. Initialization

// Initialize with default configuration
const qlra = new QLRA();

// Initialize with custom configuration
const qlra = new QLRA({
  size: 512,
  theta_c: 1.8,
  stepInterval: 50
});

2. Computation Flow

// Basic computation flow
const computePattern = async () => {
  await qlra.reset();
  const result = await qlra.compute(inputData);
  const features = await qlra.extractFeatures(result);
  return features;
};

3. Error Handling

try {
  const result = await qlra.compute(inputData);
} catch (error) {
  if (error instanceof QLRAError) {
    console.error('QLRA computation error:', error.message);
  }
}

Performance Considerations

1. Matrix Size Impact

   • Default 256×256 optimized for most use cases

   • Larger sizes increase computation complexity exponentially

2. Step Interval Optimization

   • Minimum recommended: 50ms

   • Optimal range: 100-200ms

3. Memory Management

   • Pattern history limited to last 1000 steps by default

   • Configurable through `maxHistoryLength` option

Common Use Cases

1. Pattern Recognition

2. Optimization Problems

3. Data Encoding/Decoding

4. Feature Extraction

5. Quantum-Inspired Computing

Version History

• 1.0.0: Initial release

  • Core QLRA implementation

  • Basic algorithm patterns

  • React component library