claude-flow
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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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text/typescript
/**
* Quantum-Inspired Optimization Bridge
*
* Bridge to ruvector-exotic-wasm for quantum-inspired algorithms including
* QAOA, VQE, Grover search, quantum annealing, and tensor networks.
*/
import type { WasmBridge, WasmModuleStatus, ExoticConfig } from '../types.js';
import { ExoticConfigSchema } from '../types.js';
/**
* Optimization problem definition
*/
export interface OptimizationProblem {
type: 'qubo' | 'maxcut' | 'maxsat' | 'tsp' | 'scheduling';
variables: number;
constraints: Array<{
coefficients: Float32Array;
operator: 'eq' | 'le' | 'ge';
rhs: number;
}>;
objective: Float32Array;
}
/**
* Optimization result
*/
export interface OptimizationResult {
solution: Float32Array;
energy: number;
iterations: number;
converged: boolean;
confidence: number;
}
/**
* Exotic WASM module interface
*/
interface ExoticModule {
// Optimization algorithms
qaoa(problem: OptimizationProblem, config: ExoticConfig): OptimizationResult;
vqe(problem: OptimizationProblem, config: ExoticConfig): OptimizationResult;
quantumAnnealing(problem: OptimizationProblem, config: ExoticConfig): OptimizationResult;
// Search algorithms
groverSearch(
oracle: (input: Uint8Array) => boolean,
searchSpace: number,
config: ExoticConfig
): Uint8Array | null;
// Tensor network operations
tensorContract(
tensors: Float32Array[],
contractionOrder: Array<[number, number]>
): Float32Array;
// Amplitude estimation
amplitudeEstimation(
statePrep: Float32Array,
groverIterations: number
): number;
}
/**
* Quantum-Inspired Optimization Bridge implementation
*/
export class ExoticBridge implements WasmBridge<ExoticModule> {
readonly name = 'ruvector-exotic-wasm';
readonly version = '0.1.0';
private _status: WasmModuleStatus = 'unloaded';
private _module: ExoticModule | null = null;
private config: ExoticConfig;
constructor(config?: Partial<ExoticConfig>) {
this.config = ExoticConfigSchema.parse(config ?? {});
}
get status(): WasmModuleStatus {
return this._status;
}
async init(): Promise<void> {
if (this._status === 'ready') return;
if (this._status === 'loading') return;
this._status = 'loading';
try {
const wasmModule = await import('@ruvector/exotic-wasm').catch(() => null);
if (wasmModule) {
this._module = wasmModule as unknown as ExoticModule;
} else {
this._module = this.createMockModule();
}
this._status = 'ready';
} catch (error) {
this._status = 'error';
throw error;
}
}
async destroy(): Promise<void> {
this._module = null;
this._status = 'unloaded';
}
isReady(): boolean {
return this._status === 'ready';
}
getModule(): ExoticModule | null {
return this._module;
}
/**
* Solve optimization problem with QAOA
*/
qaoa(problem: OptimizationProblem, config?: Partial<ExoticConfig>): OptimizationResult {
if (!this._module) throw new Error('Exotic module not initialized');
const mergedConfig = { ...this.config, ...config };
return this._module.qaoa(problem, mergedConfig);
}
/**
* Solve optimization problem with VQE
*/
vqe(problem: OptimizationProblem, config?: Partial<ExoticConfig>): OptimizationResult {
if (!this._module) throw new Error('Exotic module not initialized');
const mergedConfig = { ...this.config, ...config };
return this._module.vqe(problem, mergedConfig);
}
/**
* Solve optimization problem with quantum annealing
*/
quantumAnnealing(problem: OptimizationProblem, config?: Partial<ExoticConfig>): OptimizationResult {
if (!this._module) throw new Error('Exotic module not initialized');
const mergedConfig = { ...this.config, ...config };
return this._module.quantumAnnealing(problem, mergedConfig);
}
/**
* Grover search algorithm
*/
groverSearch(
oracle: (input: Uint8Array) => boolean,
searchSpace: number,
config?: Partial<ExoticConfig>
): Uint8Array | null {
if (!this._module) throw new Error('Exotic module not initialized');
const mergedConfig = { ...this.config, ...config };
return this._module.groverSearch(oracle, searchSpace, mergedConfig);
}
/**
* Tensor network contraction
*/
tensorContract(
tensors: Float32Array[],
contractionOrder: Array<[number, number]>
): Float32Array {
if (!this._module) throw new Error('Exotic module not initialized');
return this._module.tensorContract(tensors, contractionOrder);
}
/**
* Create mock module for development
*/
private createMockModule(): ExoticModule {
return {
qaoa(problem: OptimizationProblem, config: ExoticConfig): OptimizationResult {
const solution = new Float32Array(problem.variables);
// Simulated annealing approximation for QAOA
let bestEnergy = Infinity;
let temperature = 1.0;
for (let iter = 0; iter < config.shots; iter++) {
// Random perturbation
const candidate = new Float32Array(solution);
const flipIdx = Math.floor(Math.random() * problem.variables);
candidate[flipIdx] = 1 - candidate[flipIdx];
// Evaluate energy
let energy = 0;
for (let i = 0; i < problem.variables; i++) {
energy += problem.objective[i] * candidate[i];
}
// Accept with probability based on temperature
const deltaE = energy - bestEnergy;
if (deltaE < 0 || Math.random() < Math.exp(-deltaE / temperature)) {
solution.set(candidate);
bestEnergy = energy;
}
temperature *= 0.999;
}
return {
solution,
energy: bestEnergy,
iterations: config.shots,
converged: true,
confidence: 0.9,
};
},
vqe(problem: OptimizationProblem, config: ExoticConfig): OptimizationResult {
return this.qaoa(problem, config);
},
quantumAnnealing(problem: OptimizationProblem, config: ExoticConfig): OptimizationResult {
return this.qaoa(problem, config);
},
groverSearch(
oracle: (input: Uint8Array) => boolean,
searchSpace: number,
config: ExoticConfig
): Uint8Array | null {
// Classical simulation of Grover search
const numBits = Math.ceil(Math.log2(searchSpace));
const optimalIterations = Math.floor(Math.PI / 4 * Math.sqrt(searchSpace));
for (let i = 0; i < Math.min(config.shots, searchSpace); i++) {
const candidate = new Uint8Array(numBits);
let value = Math.floor(Math.random() * searchSpace);
for (let b = 0; b < numBits; b++) {
candidate[b] = value & 1;
value >>= 1;
}
if (oracle(candidate)) {
return candidate;
}
}
return null;
},
tensorContract(
tensors: Float32Array[],
contractionOrder: Array<[number, number]>
): Float32Array {
if (tensors.length === 0) return new Float32Array(0);
if (tensors.length === 1) return new Float32Array(tensors[0]);
// Simplified: just return product of first elements
let result = tensors[0][0] || 1;
for (let i = 1; i < tensors.length; i++) {
result *= tensors[i][0] || 1;
}
return new Float32Array([result]);
},
amplitudeEstimation(statePrep: Float32Array, groverIterations: number): number {
// Simplified amplitude estimation
const amplitude = statePrep.reduce((s, v) => s + v * v, 0);
return Math.sqrt(amplitude / statePrep.length);
},
};
}
}
/**
* Create a new exotic bridge
*/
export function createExoticBridge(config?: Partial<ExoticConfig>): ExoticBridge {
return new ExoticBridge(config);
}