@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