# Quantum MCP Project TODO

## Current Phase: Phase 3 - Quantum Orchestration Integration
## Current Chunk: 3.9 Complete - D-Wave Integration
## Last Updated: 2026-01-22

---

## Project Scope Change (2026-01-22)

X-DEC metric intelligence (Phase 2) has been migrated to the **predictive-insights** project,
which has a more sophisticated Temporal X-DEC implementation (BiGRU-XVAE-DEC).

This project now focuses exclusively on:
- Quantum computing integration (Azure Quantum, VQE, QAOA, Q#)
- Multi-agent orchestration (routing, consensus, decomposition)
- Quantum-enhanced orchestration (QUBO/QAOA routing)

See: `/Users/ryan.matuszewski/dev/repositories/ai/predictive-insights` for metric prediction.

---

## Phase 0 Progress - COMPLETE

### Chunk 0.1: Project Setup
- [x] 0.1.1 Create directory structure and pyproject.toml
- [x] 0.1.2 Configure dependencies and dev tools
- [x] 0.1.3 Create configuration management
- [x] 0.1.4 Verify setup with smoke test

### Chunk 0.2: Azure Quantum Client - Connection
- [x] 0.2.1 Create QuantumClient class with connection management
- [x] 0.2.2 Implement backend enumeration
- [x] 0.2.3 Add retry logic and error handling
- [x] 0.2.4 Wire up and test connection layer

### Chunk 0.3: Azure Quantum Client - Jobs
- [x] 0.3.1 Implement async job submission
- [x] 0.3.2 Implement job status polling
- [x] 0.3.3 Implement result retrieval and parsing
- [x] 0.3.4 Add cost estimation before submission
- [x] 0.3.5 Wire up and test job lifecycle

### Chunk 0.4: Circuit Building
- [x] 0.4.1 Implement Qiskit to Azure format converter
- [x] 0.4.2 Add parameter binding for variational circuits
- [x] 0.4.3 Implement simulator fallback routing
- [x] 0.4.4 Wire up and test circuit building

### Chunk 0.5: MCP Server Foundation
- [x] 0.5.1 Create MCP server skeleton using FastMCP
- [x] 0.5.2 Implement tool registration framework
- [x] 0.5.3 Add request/response handling with error propagation
- [x] 0.5.4 Wire up and test server foundation

### Chunk 0.6: Core MCP Tools - Part 1
- [x] 0.6.1 Implement quantum_list_backends
- [x] 0.6.2 Implement quantum_simulate
- [x] 0.6.3 Implement quantum_estimate_cost
- [x] 0.6.4 Wire up and test Part 1 tools

### Chunk 0.7: Core MCP Tools - Part 2
- [x] 0.7.1 Implement quantum_submit_job
- [x] 0.7.2 Implement quantum_get_results
- [x] 0.7.3 Add async job tracking across requests
- [x] 0.7.4 Wire up and test Part 2 tools

### Chunk 0.8: VQE Algorithm
- [x] 0.8.1 Implement VQE ansatz builders
- [x] 0.8.2 Implement classical optimizer integration
- [x] 0.8.3 Implement Hamiltonian encoding
- [x] 0.8.4 Implement quantum_vqe tool
- [x] 0.8.5 Wire up and test VQE

### Chunk 0.9: QAOA Algorithm
- [x] 0.9.1 Implement MaxCut encoding
- [x] 0.9.2 Implement QAOA mixer and cost Hamiltonians
- [x] 0.9.3 Implement QAOA circuit builder
- [x] 0.9.4 Implement quantum_qaoa tool
- [x] 0.9.5 Wire up and test QAOA

### Chunk 0.10: Quantum ML Kernels
- [x] 0.10.1 Implement feature map circuits (Z, ZZ, ZZZ)
- [x] 0.10.2 Implement kernel matrix computation
- [x] 0.10.3 Implement quantum_kernel tool
- [x] 0.10.4 Wire up and test kernels

### Chunk 0.11: Q# Integration
- [x] 0.11.1 Set up Q# runtime integration
- [x] 0.11.2 Implement Q# compilation and execution
- [x] 0.11.3 Implement quantum_run_qsharp tool
- [x] 0.11.4 Wire up and test Q# integration

### Chunk 0.12: Server Polish
- [x] 0.12.1 Add session management with job tracking
- [x] 0.12.2 Add result caching
- [x] 0.12.3 Add budget tracking and enforcement
- [x] 0.12.4 Wire up and test polish features

### Chunk 0.13: Documentation & Validation
- [x] 0.13.1 Create Claude Code MCP configuration (mcp.json)
- [x] 0.13.2 Create entry point (__main__.py)
- [x] 0.13.3 Update README with usage documentation
- [x] 0.13.4 Final validation

---

## Phase 1 Progress - COMPLETE

### Chunk 1.1-1.6: Agent Abstraction Layer
- [x] 1.1 Agent protocol and base classes
- [x] 1.2 Claude agent implementation
- [x] 1.3 OpenAI agent implementation
- [x] 1.4 Local/Ollama agent implementation
- [x] 1.5 Tool agent wrapper
- [x] 1.6 Task and response models

### Chunk 1.7-1.9: Routing Strategies
- [x] 1.7 Capability-based router
- [x] 1.8 Load balancing router
- [x] 1.9 Learned router with performance tracking

### Chunk 1.10-1.12: Consensus Mechanisms
- [x] 1.10 Voting consensus (single winner / majority)
- [x] 1.11 Weighted merge consensus
- [x] 1.12 Debate protocol consensus

### Chunk 1.13: Task Decomposition Engine
- [x] 1.13.1 SimpleDecomposer (sentence-based splitting)
- [x] 1.13.2 RecursiveDecomposer (depth-controlled)
- [x] 1.13.3 DomainDecomposer (capability-aware)
- [x] 1.13.4 Wire up and test decomposition

### Chunk 1.14: Collective MCP Interface
- [x] 1.14.1 Collective orchestrator class
- [x] 1.14.2 CollectiveConfig and ExecutionMode
- [x] 1.14.3 CollectiveResult with consensus tracking
- [x] 1.14.4 Wire up and test collective

### Chunk 1.15: Observability and Tracing
- [x] 1.15.1 Tracer with span context propagation
- [x] 1.15.2 MetricsCollector (counters, gauges, histograms)
- [x] 1.15.3 EventEmitter with pattern subscriptions
- [x] 1.15.4 Wire up and test observability

### Chunk 1.16: Baseline Benchmarking
- [x] 1.16.1 BenchmarkConfig and BenchmarkMetrics
- [x] 1.16.2 TaskBenchmark for execution timing
- [x] 1.16.3 BenchmarkSuite with reporting
- [x] 1.16.4 Wire up and test benchmarks

### Phase 1 Integration Tests
- [x] Orchestration integration (agent → router → consensus)
- [x] MCP server integration (tool registration, execution)
- [x] Azure Quantum job lifecycle integration

---

## Phase 2 - MIGRATED TO predictive-insights

Phase 2 (X-DEC Metric Intelligence) has been migrated to the predictive-insights project.

The predictive-insights project has a superior Temporal X-DEC implementation with:
- BiGRU encoders with temporal attention (vs MLP-based)
- Native 30-timestep sequence modeling
- 5 semantic operational state clusters
- Two-stage training with lambda annealing
- Production-ready FastAPI deployment

See: https://github.com/ryanmat/Predictive-Diagnostics

---

## Phase 3: Quantum Orchestration Integration - IN PROGRESS

### Chunk 3.1: QUBO Formulation for Routing - COMPLETE
- [x] 3.1.1 Implement QUBOFormulation class with matrix construction
- [x] 3.1.2 Implement selection constraint encoding (equality/range)
- [x] 3.1.3 Implement RouteQUBO builder with agent scores
- [x] 3.1.4 Implement classical brute-force and greedy solvers
- [x] 3.1.5 Implement QUBORouter extending Router base class
- [x] 3.1.6 Wire up factory function and exports
- [x] 3.1.7 Unit tests (17 tests)

### Chunk 3.2: QAOA Router Implementation - COMPLETE
- [x] 3.2.1 Implement QUBO-to-Ising Hamiltonian conversion
- [x] 3.2.2 Implement ising_to_qaoa_cost helper
- [x] 3.2.3 Build QAOA circuit for QUBO-derived Ising
- [x] 3.2.4 Implement QAOARouter extending QUBORouter
- [x] 3.2.5 Add QAOA optimization loop with configurable params
- [x] 3.2.6 Wire up factory function and exports
- [x] 3.2.7 Unit tests (17 tests)

| Chunk | Purpose | Status |
|-------|---------|--------|
| 3.1 | QUBO formulation for routing | COMPLETE |
| 3.2 | QAOA router implementation | COMPLETE |
| 3.3 | Quantum consensus formulation | Pending |
| 3.4 | Quantum consensus implementation | Pending |
| 3.5 | Classical coherence simulation | Pending |
| 3.6 | Hybrid orchestrator | Pending |
| 3.7 | Quantum-classical benchmarking | Pending |
| 3.8 | Coherence research experiments | Pending |
| 3.9 | D-Wave quantum annealing backend | COMPLETE |

### Chunk 3.9: D-Wave Quantum Annealing Backend - COMPLETE
- [x] 3.9.1 Create backend protocol + types (protocol.py)
- [x] 3.9.2 Create annealing protocol + results (annealing.py)
- [x] 3.9.3 Create ExactSolver fallback (exact_solver.py)
- [x] 3.9.4 Create D-Wave backend (dwave.py)
- [x] 3.9.5 Create factory functions (factory.py)
- [x] 3.9.6 Create module init + update config.py
- [x] 3.9.7 Create DWaveRouter implementation
- [x] 3.9.8 Update router factory + orchestration exports
- [x] 3.9.9 Add tests for backends and router (46 tests)
- [x] 3.9.10 Update pyproject.toml with optional dwave dependency
- [x] 3.9.11 Update docs (plan.md, todo.md)

---

## Quick Reference

| Phase | Focus | Status | Tests |
|-------|-------|--------|-------|
| Phase 0 | Quantum Foundation | COMPLETE | ~174 |
| Phase 1 | Multi-Agent Orchestration | COMPLETE | ~180 |
| Phase 2 | Metric Intelligence (X-DEC) | MIGRATED | - |
| Phase 3 | Quantum Orchestration | IN PROGRESS | 80 |

**Total Tests: 432 passing**

---

## Architecture

```
                    ┌─────────────────────────────────────┐
                    │          quantum_mcp                │
                    │                                     │
                    │  ┌─────────────────────────────┐   │
                    │  │     Quantum Computing       │   │
                    │  │  VQE | QAOA | Kernels | Q#  │   │
                    │  │       (Phase 0)             │   │
                    │  └─────────────┬───────────────┘   │
                    │                │                    │
                    │  ┌─────────────┴───────────────┐   │
                    │  │   Multi-Agent Orchestration │   │
                    │  │   Routing | Consensus       │   │
                    │  │   Decomposition             │   │
                    │  │       (Phase 1)             │   │
                    │  └─────────────┬───────────────┘   │
                    │                │                    │
                    │  ┌─────────────┴───────────────┐   │
                    │  │   Quantum Orchestration     │   │
                    │  │   QUBO | QAOA | D-Wave      │   │
                    │  │       (Phase 3)             │   │
                    │  └─────────────┬───────────────┘   │
                    │                │                    │
                    │  ┌─────────────┴───────────────┐   │
                    │  │   Backend Abstraction       │   │
                    │  │   Azure Quantum | D-Wave    │   │
                    │  │       (Phase 3.9)           │   │
                    │  └─────────────┬───────────────┘   │
                    │                │                    │
                    │         ┌──────┴──────┐            │
                    │         │  MCP Server │            │
                    │         └──────┬──────┘            │
                    └────────────────┼────────────────────┘
                                     │
                              ┌──────┴──────┐
                              │ Claude Code │
                              └─────────────┘
```

---

## Future Integration with predictive-insights

When predictive-insights reaches Phase 14 (MoE Unification), quantum_mcp can provide:

1. **QAOA-optimized expert routing** - Quantum optimization for sparse expert selection
2. **Multi-agent consensus** - When multiple prediction experts disagree
3. **Quantum kernel enhancement** - Improve clustering quality

See: docs/integration_with_predictive_insights.md

---

## Notes

### Architecture Decisions
- Phase 0: Azure Quantum MCP server with VQE, QAOA, kernels, Q#
- Phase 1: Multi-agent orchestration with routing, consensus, decomposition
- Phase 2: Migrated to predictive-insights (superior implementation)
- Phase 3: Quantum-enhanced routing using QUBO/QAOA
- Phase 3.9: Multi-backend abstraction (Azure Quantum gate-based + D-Wave annealing)

### Configuration
- Azure Quantum: Workspace rm-quantum configured with Contributor role
- Service Principal: 62a49f65-acf7-46da-a04a-d4679512ea53

### Next Steps
1. Complete Phase 3.3-3.8 (Quantum Consensus, Hybrid Orchestrator)
2. Future: Integration with predictive-insights MoE architecture
