@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

$3

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

$3

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

Quick Start

`typescript
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

$3

Solve combinatorial optimization problems using simulated quantum annealing.

`typescript
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.

$3

Approximate optimization using Quantum Approximate Optimization Algorithm emulation.

`typescript
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

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

`typescript
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.

$3

Resolve complex dependency graphs using quantum optimization.

`typescript
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.

$3

Quantum-optimized task scheduling for complex workflows.

`typescript
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

`typescript
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

$3

| 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 |

$3

| 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 |

$3

| 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

$3

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

$3

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

$3

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