ruvnet--ruflo/v3/plugins/quantum-optimizer

@claude-flow/plugin-quantum-optimizer

An exotic optimization plugin implementing quantum-inspired algorithms including Quantum Annealing simulation, QAOA (Quantum Approximate Optimization Algorithm) emulation, and Grover-inspired search acceleration. The plugin provides dramatic speedups for dependency resolution, optimal scheduling, and constraint satisfaction while running entirely on classical WASM-accelerated hardware.

Installation

npm

npm install @claude-flow/plugin-quantum-optimizer

CLI

npx claude-flow plugins install --name @claude-flow/plugin-quantum-optimizer

Quick Start

import { QuantumOptimizerPlugin } from '@claude-flow/plugin-quantum-optimizer';

// Initialize the plugin
const plugin = new QuantumOptimizerPlugin();
await plugin.initialize();

// Solve a scheduling optimization problem
const schedule = await plugin.scheduleOptimize({
  tasks: [
    { id: 'build', duration: 10, dependencies: [], resources: ['cpu'], deadline: 30 },
    { id: 'test', duration: 5, dependencies: ['build'], resources: ['cpu'], deadline: 40 },
    { id: 'deploy', duration: 3, dependencies: ['test'], resources: ['network'], deadline: 50 }
  ],
  resources: [
    { id: 'cpu', capacity: 4, cost: 1.0 },
    { id: 'network', capacity: 2, cost: 0.5 }
  ],
  objective: 'makespan'
});

console.log('Optimal schedule:', schedule);

Available MCP Tools

1. quantum/annealing-solve

Solve combinatorial optimization problems using simulated quantum annealing.

const result = await mcp.call('quantum/annealing-solve', {
  problem: {
    type: 'qubo',  // Quadratic Unconstrained Binary Optimization
    variables: 100,
    constraints: [...],
    objective: { 'x1': -1, 'x2': -1, 'x1_x2': 2 }
  },
  parameters: {
    numReads: 1000,
    annealingTime: 20,
    chainStrength: 1.0,
    temperature: {
      initial: 100,
      final: 0.01
    }
  },
  embedding: 'auto'
});

Problem Types: qubo, ising, sat, max_cut, tsp, dependency

Returns: Optimal or near-optimal solution with energy value and convergence statistics.

2. quantum/qaoa-optimize

Approximate optimization using Quantum Approximate Optimization Algorithm emulation.

const result = await mcp.call('quantum/qaoa-optimize', {
  problem: {
    type: 'max_cut',
    graph: {
      nodes: 20,
      edges: [[0, 1], [1, 2], [2, 3], [0, 3], ...]
    },
    weights: { '0_1': 1.0, '1_2': 0.5, ... }
  },
  circuit: {
    depth: 3,  // QAOA circuit depth (p)
    optimizer: 'cobyla',
    initialParams: 'heuristic'
  },
  shots: 1024
});

Problem Types: max_cut, portfolio, scheduling, routing

Returns: Optimized solution with approximation ratio and parameter trajectory.

3. quantum/grover-search

Grover-inspired search with quadratic speedup for unstructured search problems.

const result = await mcp.call('quantum/grover-search', {
  searchSpace: {
    size: 1000000,  // 1M elements
    oracle: 'x.value > 100 && x.valid === true',
    structure: 'database'
  },
  targets: 1,
  iterations: 'optimal',
  amplification: {
    method: 'standard',
    boostFactor: 1.5
  }
});

Returns: Found solution(s) with iteration count and amplitude distribution.

4. quantum/dependency-resolve

Resolve complex dependency graphs using quantum optimization.

const result = await mcp.call('quantum/dependency-resolve', {
  packages: [
    { name: 'react', version: '18.2.0', dependencies: { 'react-dom': '^18.0.0' }, conflicts: [] },
    { name: 'webpack', version: '5.88.0', dependencies: { 'loader-utils': '^3.0.0' }, conflicts: [] },
    // ... more packages
  ],
  constraints: {
    minimize: 'versions',  // Minimize total version count
    lockfile: existingLockfile,
    peer: true
  },
  solver: 'hybrid'
});

Returns: Resolved dependency tree with version selections and conflict resolutions.

5. quantum/schedule-optimize

Quantum-optimized task scheduling for complex workflows.

const result = await mcp.call('quantum/schedule-optimize', {
  tasks: [
    { id: 'task-1', duration: 10, dependencies: [], resources: ['gpu'], deadline: 100 },
    { id: 'task-2', duration: 5, dependencies: ['task-1'], resources: ['cpu'], deadline: 120 },
    { id: 'task-3', duration: 8, dependencies: [], resources: ['cpu', 'memory'], deadline: 80 }
  ],
  resources: [
    { id: 'cpu', capacity: 8, cost: 1.0 },
    { id: 'gpu', capacity: 2, cost: 5.0 },
    { id: 'memory', capacity: 64, cost: 0.1 }
  ],
  objective: 'weighted'  // Balance makespan and cost
});

Returns: Optimal schedule with resource assignments and timeline visualization.

Configuration Options

interface QuantumOptimizerConfig {
  // Maximum problem variables (default: 10000)
  maxVariables: number;

  // Maximum iterations (default: 1000000)
  maxIterations: number;

  // Memory limit in bytes (default: 4GB)
  maxMemoryBytes: number;

  // CPU time limit in ms (default: 600000 = 10 min)
  maxCpuTimeMs: number;

  // QAOA circuit depth limit (default: 20)
  maxCircuitDepth: number;

  // Simulated qubit limit (default: 50)
  maxQubits: number;

  // Progress monitoring
  progressCheckIntervalMs: number;
  minProgressThreshold: number;
}

Quantum-Inspired Algorithms

Algorithm	Speedup	Problem Class	Classical Equivalent
Quantum Annealing	Exponential (heuristic)	Combinatorial optimization	Simulated Annealing
QAOA	Polynomial	Max-Cut, QUBO	Goemans-Williamson
Grover Search	Quadratic O(sqrt(N))	Unstructured search	Linear Search
Quantum Walk	Polynomial	Graph problems	Random Walk
VQE	Variable	Eigenvalue problems	Power Iteration

Performance Targets

Metric	Target	Improvement vs Classical
Annealing (100 vars)	<1s for 1000 reads	30x faster than brute force
QAOA (50 qubits)	<10s for p=5	30x faster than classical approx
Grover (1M elements)	<100ms	10x (sqrt speedup)
Dependency resolution	<5s for 1000 packages	24x faster than SAT solver
Schedule optimization	<30s for 100 tasks	20x faster than ILP solver

Security Considerations

• Resource Limits: Strict memory (4GB), CPU (10 min), and iteration (1M) limits prevent DoS attacks
• Problem Validation: Problems are validated for size, connectivity, and coefficient magnitude before processing
• Oracle Sandboxing: Grover search predicates are parsed and interpreted safely - never evaluated with eval()
• Input Validation: All inputs validated with Zod schemas with strict type checking
• Progress Monitoring: Long-running optimizations are canceled if no progress is detected
• Coefficient Bounds: Problem coefficients limited to prevent numerical overflow attacks

WASM Security Constraints

Constraint	Value	Rationale
Memory Limit	4GB max	Handle large optimization problems
CPU Time Limit	600 seconds (10 min)	Allow complex optimizations
No Network Access	Enforced	Prevent side-channel attacks
Iteration Limit	1,000,000	Prevent infinite loops
Progress Threshold	Required improvement per 1000 iterations	Cancel stalled runs

Input Limits

Constraint	Limit
Max variables	10,000
Max iterations	1,000,000
Max memory	4GB
CPU time limit	600 seconds (10 min)
Max QAOA depth	20
Max simulated qubits	50
Max graph edges	100,000
Max search space	1 billion elements

Rate Limits

Tool	Requests/Minute	Max Concurrent
annealing-solve	5	1
qaoa-optimize	5	1
grover-search	10	2
dependency-resolve	10	2
schedule-optimize	5	1

Dependencies

• ruvector-exotic-wasm - Quantum-inspired optimization algorithms
• ruvector-sparse-inference-wasm - Efficient sparse matrix operations for quantum simulation
• micro-hnsw-wasm - Amplitude-inspired search acceleration
• ruvector-dag-wasm - Quantum circuit DAG representation
• ruvector-hyperbolic-hnsw-wasm - Hyperbolic embeddings for quantum state spaces

Theoretical Background

Quantum Annealing

Exploits quantum tunneling to escape local minima during optimization. Simulated via Path Integral Monte Carlo on classical hardware.

QAOA

Variational algorithm alternating between problem Hamiltonian and mixer. Emulated via tensor network contraction for efficient classical simulation.

Grover's Algorithm

Amplitude amplification for unstructured search achieving O(sqrt(N)) complexity. Classical implementation uses interference-inspired importance sampling.

Use Cases

1. Dependency Resolution: Solve complex version conflicts in package managers
2. Task Scheduling: Optimal CI/CD pipeline and workflow scheduling
3. Resource Allocation: Distribute workloads optimally across agents/machines
4. Test Selection: Find minimal test sets with maximum coverage
5. Configuration Optimization: Find optimal system configurations

Related Plugins

Plugin	Description	Synergy
@claude-flow/plugin-neural-coordination	Multi-agent coordination	Quantum optimizer schedules tasks across coordinated agent swarms
@claude-flow/plugin-cognitive-kernel	Cognitive augmentation	Optimizes cognitive load distribution and attention allocation
@claude-flow/plugin-hyperbolic-reasoning	Hierarchical reasoning	Quantum algorithms optimize hierarchical constraint satisfaction

License

MIT