# Quantum MCP Implementation Plan

## Overview

This plan implements the Quantum MCP Server - a quantum computing and multi-agent orchestration platform accessible via the Model Context Protocol (MCP). The server provides Claude Code with access to Azure Quantum computing resources and intelligent agent coordination.

**Scope Change (2026-01-22):** Metric intelligence (X-DEC) has been migrated to the predictive-insights project. This project now focuses exclusively on quantum computing and multi-agent orchestration.

**Principles:**
- Test-driven development (tests first or alongside)
- Incremental progress (each prompt builds on previous)
- No orphaned code (everything wires together)
- Right-sized steps (15-45 min implementation, independently testable)

---

## Phase 0: Quantum Foundation - COMPLETE

**Goal:** Azure Quantum accessible via MCP for research acceleration
**Deliverable:** Working MCP server exposing Azure Quantum to Claude

### MCP Tools Delivered (8 total)

| Tool | Purpose |
|------|---------|
| `ping` | Server connectivity test |
| `quantum_list_backends` | Azure Quantum backend enumeration |
| `quantum_simulate` | Local Qiskit circuit simulation |
| `quantum_estimate_cost` | Cost estimation before job submission |
| `quantum_vqe` | Variational Quantum Eigensolver algorithm |
| `quantum_qaoa` | Quantum Approximate Optimization Algorithm |
| `quantum_kernel` | Quantum ML kernel computation |
| `quantum_run_qsharp` | Q# program execution |

### Success Criteria - ALL MET
- [x] Claude can explain quantum concepts and generate Q# code
- [x] Claude can submit quantum jobs and interpret results
- [x] Claude can run VQE and QAOA for optimization problems
- [x] Claude can compute quantum kernels for ML applications
- [x] Costs are tracked and controllable
- [x] Simulator fallback works seamlessly

---

## Phase 1: Multi-Agent Orchestration - COMPLETE

**Goal:** Working multi-agent system with classical orchestration
**Deliverable:** Agent collective with routing, consensus, and observability

### Components Delivered

**Agent Abstraction Layer:**
- Agent protocol and base classes
- Claude, OpenAI, Local/Ollama agent implementations
- Tool agent wrapper
- Task and response models

**Routing Strategies:**
- Capability-based router
- Load balancing router
- Learned router with performance tracking

**Consensus Mechanisms:**
- Voting consensus (single winner / majority)
- Weighted merge consensus
- Debate protocol consensus

**Task Decomposition:**
- SimpleDecomposer (sentence-based)
- RecursiveDecomposer (depth-controlled)
- DomainDecomposer (capability-aware)

**Observability:**
- Tracer with span context propagation
- MetricsCollector (counters, gauges, histograms)
- EventEmitter with pattern subscriptions
- BenchmarkSuite with reporting

---

## Phase 2: Metric Intelligence - MIGRATED

**Status:** Migrated to predictive-insights project

The X-DEC metric intelligence layer has been moved to the predictive-insights project, which has a superior Temporal X-DEC implementation:

- 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

**Repository:** https://github.com/ryanmat/Predictive-Diagnostics

---

## Phase 3: Quantum Orchestration Integration - IN PROGRESS

**Goal:** Quantum-enhanced coordination replacing classical
**Deliverable:** Hybrid quantum/classical orchestrator

### Completed Chunks

| Chunk | Purpose | Status |
|-------|---------|--------|
| 3.1 | QUBO formulation for routing | COMPLETE |
| 3.2 | QAOA router implementation | COMPLETE |
| 3.9 | D-Wave quantum annealing backend | COMPLETE |

### Phase 3.9: D-Wave Integration (Completed)

Multi-backend quantum abstraction supporting both gate-based (Azure Quantum) and annealing (D-Wave) paradigms:

**New Module: `src/quantum_mcp/backends/`**
- `protocol.py` - Backend protocols and types (BackendParadigm, BackendStatus, QuantumBackend)
- `annealing.py` - Annealing protocol and data models (AnnealingSample, AnnealingResult)
- `exact_solver.py` - Classical brute-force/greedy solver (ExactSolverBackend)
- `dwave.py` - D-Wave Leap integration (DWaveBackend)
- `factory.py` - Backend factory functions (create_annealing_backend)

**DWaveRouter** (`src/quantum_mcp/orchestration/dwave_router.py`):
- Extends QUBORouter with D-Wave backend support
- Lazy backend initialization with graceful fallback
- QUBO matrix to dict conversion for annealing
- Bitstring decoding for agent selection

**Configuration** (`.env`):
```bash
DWAVE_API_TOKEN=DEV-xxx...  # D-Wave Leap token
DWAVE_SOLVER=Advantage_system6.4  # Optional
```

**Usage**:
```python
router = create_router("dwave", agents)
decision = await router.route(task)
```

**Tests**: 46 new tests (26 backends + 20 router)

### Pending Chunks

| Chunk | Purpose | Description |
|-------|---------|-------------|
| 3.3 | Quantum consensus formulation | QUBO for consensus optimization |
| 3.4 | Quantum consensus implementation | QAOA-based consensus |
| 3.5 | Classical coherence simulation | Baseline coherence metrics |
| 3.6 | Hybrid orchestrator | Combined quantum/classical |
| 3.7 | Quantum-classical benchmarking | Performance comparison |
| 3.8 | Coherence research experiments | Research findings |

---

## Phase 4: Emergent Behavior Research

**Goal:** Systematic exploration of collective capabilities
**Status:** Future (after Phase 3)
**Deliverable:** Research findings and architecture refinements

---

## Phase 5: Application & Validation

**Goal:** Real-world proof-of-concept with LogicMonitor use cases
**Status:** Future (after Phase 4)
**Deliverable:** Working applications demonstrating practical value

---

## 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 in DEC layer

See: docs/integration_with_predictive_insights.md

---

## Document History

- v1.0: Initial plan with Phase 0 detailed
- v2.0: Phase 0, 1 complete; Phase 2 migrated to predictive-insights
- v2.1: Updated to reflect quantum-only focus (2026-01-22)
