ruvnet/NAPI-QUDAG.md

## NAPI-QUDAG.md

      
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              NAPI-QUDAG.md
            
          
    QuDAG N-API Integration Plan

Comprehensive production deployment strategy for quantum-enhanced DAG execution with native performance and npm distribution.
Architecture Overview

Core Stack

Rust backend via napi-rs for quantum DAG operations
Multi-package npm distribution (SDK, CLI, MCP servers)
Development orchestration via existing agentic tooling
Zero-copy buffer sharing for quantum state vectors

Package Structure

1. @qudag/core (N-API SDK)

Build Configuration
# Cargo.toml
[package]
name = "qudag-core"
version = "0.1.0"

[lib]
crate-type = ["cdylib"]

[dependencies]
napi = "3.0"
napi-derive = "3.0"
serde = { version = "1.0", features = ["derive"] }
tokio = { version = "1", features = ["full"] }
Core Exports
// index.d.ts
export class QuantumDAG {
  constructor(config: DAGConfig);
  addNode(node: QuantumNode): Promise<string>;
  execute(nodeId: string): Promise<ExecutionResult>;
  optimize(): Promise<OptimizationMetrics>;
  getQuantumState(): Uint8Array; // Zero-copy buffer
}

export class QuantumNode {
  constructor(operation: QuantumOperation);
  setDependencies(deps: string[]): void;
  getComplexity(): number;
}

export interface DAGConfig {
  backend: 'cpu' | 'cuda' | 'rocm';
  optimizationLevel: number;
  memoryLimit?: number;
}
Rust Implementation Highlights
#[napi]
pub struct QuantumDAG {
  dag: Arc<RwLock<DAGEngine>>,
  runtime: Runtime,
}

#[napi]
impl QuantumDAG {
  #[napi(constructor)]
  pub fn new(config: DAGConfig) -> Result<Self> {
    // Initialize quantum backend
  }
  
  #[napi]
  pub async fn execute(&self, node_id: String) -> Result<ExecutionResult> {
    // Async execution with tokio runtime
  }
  
  #[napi(getter)]
  pub fn get_quantum_state(&self) -> Result<Buffer> {
    // Zero-copy buffer export via TypedArray
  }
}
2. @qudag/cli

npx Entry Point
{
  "name": "@qudag/cli",
  "bin": {
    "qudag": "./dist/cli.js"
  },
  "dependencies": {
    "@qudag/core": "workspace:*",
    "commander": "^12.0.0",
    "ora": "^8.0.0"
  }
}
CLI Structure
#!/usr/bin/env node
import { Command } from 'commander';
import { QuantumDAG } from '@qudag/core';

const program = new Command();

program
  .name('qudag')
  .description('Quantum DAG execution engine')
  .version('0.1.0');

program
  .command('exec <file>')
  .option('-b, --backend <type>', 'Backend: cpu|cuda|rocm')
  .option('-O <level>', 'Optimization level 0-3')
  .action(async (file, opts) => {
    const dag = new QuantumDAG({
      backend: opts.backend || 'cpu',
      optimizationLevel: parseInt(opts.O) || 2
    });
    // Execute DAG from file
  });

program
  .command('optimize <file>')
  .description('Analyze and optimize quantum DAG')
  .action(async (file) => {
    // DAG optimization analysis
  });

program.parse();
3. @qudag/mcp-stdio

MCP Server Implementation
// stdio-server.ts
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { QuantumDAG } from '@qudag/core';

const server = new Server({
  name: 'qudag-mcp',
  version: '0.1.0',
}, {
  capabilities: {
    tools: {},
    resources: {}
  }
});

// Tool: Execute Quantum DAG
server.setRequestHandler('tools/call', async (request) => {
  if (request.params.name === 'execute_quantum_dag') {
    const { dag_config, nodes } = request.params.arguments;
    const dag = new QuantumDAG(dag_config);
    
    // Build and execute DAG
    for (const node of nodes) {
      await dag.addNode(node);
    }
    
    const result = await dag.execute(nodes[0].id);
    return {
      content: [{
        type: 'text',
        text: JSON.stringify(result, null, 2)
      }]
    };
  }
});

// Resource: Quantum state visualization
server.setRequestHandler('resources/read', async (request) => {
  if (request.params.uri === 'qudag://state') {
    const dag = globalDAGRegistry.get(request.params.id);
    const state = dag.getQuantumState();
    return {
      contents: [{
        uri: request.params.uri,
        mimeType: 'application/octet-stream',
        blob: state.toString('base64')
      }]
    };
  }
});

const transport = new StdioServerTransport();
await server.connect(transport);
4. @qudag/mcp-sse

SSE Server for Web Integration
// sse-server.ts
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { SSEServerTransport } from '@modelcontextprotocol/sdk/server/sse.js';
import express from 'express';

const app = express();
const server = new Server({
  name: 'qudag-mcp-sse',
  version: '0.1.0'
}, {
  capabilities: {
    tools: {},
    resources: {},
    prompts: {}
  }
});

// Same handlers as STDIO version

app.get('/sse', async (req, res) => {
  const transport = new SSEServerTransport('/message', res);
  await server.connect(transport);
});

app.post('/message', async (req, res) => {
  // Handle MCP messages
});

app.listen(3000);
Development Swarm Integration

AgenticDB Integration

# Initialize AgenticDB for quantum state persistence
npx agenticdb init --config qudag.db.toml

# Store quantum computation results
npx agenticdb store \
  --key "dag:execution:${dag_id}" \
  --value "${quantum_state}" \
  --metadata '{"backend":"cuda","qubits":64}'

# Query optimization patterns
npx agenticdb query \
  --filter "metadata.optimization_level > 2" \
  --limit 100
AgenticDB Schema
-- Store DAG execution history
CREATE TABLE quantum_executions (
  id TEXT PRIMARY KEY,
  dag_structure BLOB,
  quantum_state BLOB,
  metrics JSON,
  timestamp INTEGER
);

CREATE INDEX idx_metrics ON quantum_executions(
  json_extract(metrics, '$.execution_time_ms')
);
Agentic-Flow Orchestration

# Development workflow coordination
npx agentic-flow run --flow qudag-dev.yaml

# Parallel testing across backends
npx agentic-flow exec \
  --parallel \
  --tasks "test:cpu,test:cuda,test:rocm" \
  --max-workers 3

# Continuous benchmarking
npx agentic-flow watch \
  --trigger "src/**/*.rs" \
  --action "cargo build --release && npm run bench"
Flow Configuration
# qudag-dev.yaml
name: QuDAG Development Swarm
version: 1.0

agents:
  - name: rust-builder
    type: compiler
    config:
      watch: ["src/**/*.rs", "Cargo.toml"]
      build_cmd: "cargo build --release"
      artifacts: ["target/release/*.node"]
  
  - name: typescript-validator
    type: linter
    config:
      watch: ["**/*.ts"]
      commands: ["tsc --noEmit", "eslint"]
  
  - name: benchmark-runner
    type: performance
    config:
      baseline: "benchmarks/baseline.json"
      threshold: 0.95  # 5% regression tolerance

flows:
  - name: build-and-test
    steps:
      - agent: rust-builder
        action: build
      - agent: typescript-validator
        action: validate
      - agent: benchmark-runner
        action: compare
Claude-Flow Integration

# AI-assisted development
npx claude-flow init --project qudag

# Quantum algorithm optimization
npx claude-flow ask \
  --context "src/quantum/dag.rs" \
  "Optimize this quantum circuit for reduced gate count"

# Documentation generation
npx claude-flow generate-docs \
  --input "src/" \
  --output "docs/api/" \
  --format markdown
Claude-Flow Task Definitions
// .claude-flow/tasks.ts
export const tasks = {
  optimize_quantum_circuit: {
    prompt: `Analyze quantum DAG and suggest optimizations:
    - Gate fusion opportunities
    - Parallelization potential
    - Memory access patterns`,
    model: 'claude-sonnet-4-5',
    temperature: 0.2
  },
  
  generate_test_cases: {
    prompt: `Generate comprehensive test cases for quantum operations:
    - Edge cases for qubit counts
    - Error handling scenarios
    - Performance benchmarks`,
    output: 'tests/generated/'
  }
};
Build Pipeline

napi-rs Configuration

# .napi/config.toml
[package]
name = "qudag-core"

[build]
targets = [
  "x86_64-unknown-linux-gnu",
  "x86_64-apple-darwin",
  "aarch64-apple-darwin",
  "x86_64-pc-windows-msvc"
]

[features]
cuda = ["cudarc"]
rocm = ["hip-sys"]
Build Script
// package.json
{
  "scripts": {
    "build": "napi build --platform --release",
    "build:cuda": "napi build --platform --release --features cuda",
    "build:debug": "napi build --platform",
    "artifacts": "napi artifacts",
    "prepublishOnly": "napi prepublish -t npm"
  }
}
GitHub Actions Workflow

# .github/workflows/build.yml
name: Build Native Modules

on: [push, pull_request]

jobs:
  build:
    strategy:
      matrix:
        settings:
          - host: ubuntu-latest
            target: x86_64-unknown-linux-gnu
            build: npm run build
          - host: macos-latest
            target: x86_64-apple-darwin
            build: npm run build
          - host: macos-latest
            target: aarch64-apple-darwin
            build: npm run build
          - host: windows-latest
            target: x86_64-pc-windows-msvc
            build: npm run build
    
    runs-on: ${{ matrix.settings.host }}
    
    steps:
      - uses: actions/checkout@v4
      - uses: dtolnay/rust-toolchain@stable
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      
      - name: Install dependencies
        run: npm install
      
      - name: Build
        run: ${{ matrix.settings.build }}
      
      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: bindings-${{ matrix.settings.target }}
          path: "*.node"
Testing Strategy

Unit Tests (Rust)

#[cfg(test)]
mod tests {
  use super::*;
  
  #[test]
  fn test_dag_creation() {
    let dag = QuantumDAG::new(DAGConfig {
      backend: Backend::CPU,
      optimization_level: 2,
      memory_limit: None,
    }).unwrap();
    
    assert!(dag.dag.read().unwrap().node_count() == 0);
  }
  
  #[tokio::test]
  async fn test_async_execution() {
    let dag = create_test_dag();
    let result = dag.execute("node_1".into()).await.unwrap();
    assert!(result.success);
  }
}
Integration Tests (TypeScript)

// tests/integration/dag-execution.test.ts
import { describe, it, expect } from 'vitest';
import { QuantumDAG, QuantumNode } from '@qudag/core';

describe('Quantum DAG Execution', () => {
  it('executes simple 2-qubit circuit', async () => {
    const dag = new QuantumDAG({
      backend: 'cpu',
      optimizationLevel: 2
    });
    
    const hadamard = new QuantumNode({
      type: 'hadamard',
      qubits: [0]
    });
    
    const cnot = new QuantumNode({
      type: 'cnot',
      qubits: [0, 1]
    });
    
    const h_id = await dag.addNode(hadamard);
    const cnot_id = await dag.addNode(cnot);
    
    const result = await dag.execute(cnot_id);
    expect(result.success).toBe(true);
    expect(result.qubits).toBe(2);
  });
  
  it('handles zero-copy buffer transfer', () => {
    const dag = new QuantumDAG({ backend: 'cpu', optimizationLevel: 1 });
    const state = dag.getQuantumState();
    
    expect(state).toBeInstanceOf(Uint8Array);
    expect(state.length).toBeGreaterThan(0);
  });
});
MCP Tests

// tests/mcp/stdio-server.test.ts
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';
import { spawn } from 'child_process';

describe('MCP STDIO Server', () => {
  it('executes quantum DAG via MCP tool', async () => {
    const serverProcess = spawn('node', ['dist/stdio-server.js']);
    
    const transport = new StdioClientTransport({
      command: 'node',
      args: ['dist/stdio-server.js']
    });
    
    const client = new Client({
      name: 'test-client',
      version: '1.0.0'
    }, {
      capabilities: {}
    });
    
    await client.connect(transport);
    
    const result = await client.request({
      method: 'tools/call',
      params: {
        name: 'execute_quantum_dag',
        arguments: {
          dag_config: { backend: 'cpu', optimizationLevel: 2 },
          nodes: [/* test nodes */]
        }
      }
    });
    
    expect(result.content[0].type).toBe('text');
  });
});
Performance Benchmarks

// benchmarks/dag-performance.bench.ts
import { bench, describe } from 'vitest';
import { QuantumDAG } from '@qudag/core';

describe('DAG Performance', () => {
  bench('10-qubit circuit execution', async () => {
    const dag = createBenchmarkDAG(10);
    await dag.execute('final_node');
  });
  
  bench('100-node DAG optimization', async () => {
    const dag = createComplexDAG(100);
    await dag.optimize();
  });
  
  bench('Zero-copy state access', () => {
    const dag = createBenchmarkDAG(20);
    const state = dag.getQuantumState();
    // Access without copy
  });
});
Validation Plan

Phase 1: Core Functionality (Week 1)


N-API bindings compilation across platforms
Basic DAG construction and execution
Memory management and leak detection
Zero-copy buffer validation

Phase 2: CLI Integration (Week 2)


npx qudag command execution
File I/O and serialization
Error handling and reporting
Cross-platform compatibility

Phase 3: MCP Servers (Week 3)


STDIO transport validation
SSE transport testing
Tool invocation correctness
Resource streaming

Phase 4: Swarm Integration (Week 4)


AgenticDB persistence layer
Agentic-flow orchestration
Claude-flow AI assistance
End-to-end workflow validation

Phase 5: Production Hardening (Week 5)


Load testing (1M+ DAG nodes)
Memory profiling
Security audit
Documentation completion

Deployment

NPM Publishing

# Build all platforms
npm run build:all

# Generate artifacts
npm run artifacts

# Publish packages
npm publish --access public @qudag/core
npm publish --access public @qudag/cli
npm publish --access public @qudag/mcp-stdio
npm publish --access public @qudag/mcp-sse
Installation Paths

# SDK for developers
npm install @qudag/core

# CLI tool
npx @qudag/cli exec circuit.json

# MCP servers
npx @qudag/mcp-stdio  # For Claude Desktop
node node_modules/@qudag/mcp-sse/dist/server.js  # Web apps
Performance Targets


DAG Construction: <1ms per node
Execution Latency: <100μs overhead vs raw Rust
Memory Overhead: <5% vs native
Zero-Copy Success: 99%+ buffer transfers
NPM Install Time: <10s on commodity hardware

Key Insight: N-API enables true production deployment - your quantum algorithms run at native speeds while npm delivers instant global distribution. The swarm tools (agenticdb, agentic-flow, claude-flow) transform development from manual coordination into autonomous orchestration.
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