In neutral atom quantum computers, there exists a massive "compilation gap" between high-level scientific objectives (such as implementing a quantum error correction code) and the ability to generate efficient, high-fidelity physical control instructions that can run on real hardware. AtomQuampiler is designed as an intelligent, automated solution to bridge this gap.

AtomQuampiler constructs a complete, full-stack compilation and optimization pipeline. Users simply input a quantum error correction code in natural language, and AtomQuampiler initiates an intelligent workflow composed of multiple MCP tools:

• Generate comprehensive introductions to quantum error correction codes
• Create quantum circuits and simulate quantum code error correction cycles
• Implement decoder-based quantum code distance analysis

• Utilize reinforcement learning to explore and generate efficient logical quantum state encoding circuits
• Perform multi-dimensional comparison (fidelity, gate count) with traditionally generated circuits
• Apply optimization functions to simplify the optimal circuits

• Analyze and calibrate real hardware noise characteristics from simulated experimental data
• Extract physical parameters (gate fidelity, SPAM errors) and feed them back to upper layers
• Enable more accurate quantum circuit evaluation

• After selecting the optimal logical circuit, dive deeper into the physical layer
• Complete pulse design, execute timing planning, and generate final physical control instructions

• CZ gate (GRAPE): Bidirectional evolution, live pulse shape/fidelity plots.
• X gate (Robust GRAPE, Fourier): Envelope shaping, 3×3 optimization grid, 11×11 robustness map, Bloch‑sphere trajectories.

• RL Policies: DQN/PPO/VPG support through Experiment helpers and pre‑trained models
• Baselines: Multiple Qiskit synthesizers (AG, BM, greedy, Bravyi) for comparison
• Timeline Plotting: Per‑qubit execution visualization for bottleneck analysis
• Transpile Simplification: Depth reduction over a constrained basis
• Fidelity Simulation: Physical model or calibrated model based on analyzed benchmark data

• Circuit construction: Build syndrome measurement circuits from stabilizer generators.
• Error analysis: Calculate logical error rates vs physical error rates.
• Decoder comparison: MWPM and BP-OSD decoder performance analysis.
• Circuit visualization: Generate and visualize QEC circuits with noise.

• Code Definition: Generate stabilizer generators and logical operators for various QEC codes
• Distance Calculation: Compute code distance using integer programming
• Supported Codes: Surface codes, toric codes, Shor codes, Steane codes, and more

• Real-time Dashboard: PyQt6-based monitoring center with live updates
• Progress Tracking: Live fidelity traces, optimization progress, and status updates
• Visualization: Primary result images, circuit diagrams, and timeline plots
• Flask Integration: Local endpoint for tool communication and data streaming

AtomQuampiler/
├── src/gate_optimize/
│   ├── pulse/            # Hardware pulse optimization
│   ├── circuit/          # ML circuit optimization
│   ├── qec/              # Error correction utilities
│   ├── server.py         # MCP tools implementation
│   ├── __init__.py       # GUI + MCP stdio launcher (main)
│   └── __main__.py       # python -m gate_optimize entry
├── model/
│   ├── plots/            # Trained RL policies and training artifacts
│   └── eval/             # Benchmarks/params (e.g., 7‑bit GHZ/Steane)
├── pyproject.toml        # Dependencies and console script
└── juliamcp
    ├── server.jl         # Julia server script
    └── Project.toml      # Julia dependencies

All tools stream progress and images to the GUI and return results to the MCP client, creating a seamless intelligent workflow.

• get_code_stabilizers (Julia): Generate stabilizer generators for various QEC codes
• get_code_logicals (Julia): Extract logical operators for QEC codes
• compute_code_distance (Julia): Calculate code distance using integer programming
• analyze_qec_logical_error_rate (QEC): Analyze QEC codes with logical error rate calculation and decoder comparison

• generate_circuits (circuit): Create RL-optimized and Qiskit baseline circuits from stabilizer generators
• simplify_best_circuit (circuit): Optimize the best circuit using transpile for depth reduction
• plot_timeline (circuit): Visualize per‑qubit execution timelines for bottleneck analysis

• simulate_gate_benchmark_data (calibration): Generate standardized RB‑style synthetic data for X and CZ gates
• analyze_gate_fidelity_from_data (calibration): Fit benchmark data to extract calibrated 1Q/2Q fidelities and SPAM error
• compare_circuits_fidelity (circuit): Evaluate circuits under physical noise model or calibrated error rates

• optimize_cz_gate (pulse): GRAPE CZ gate optimization with live pulse/fidelity visualization
• optimize_x_gate (pulse): Robust Fourier‑parameterized X gate with robustness analysis and Bloch‑sphere trajectories

1. Code Definition: Use Julia MCP tools to generate stabilizer generators for your target QEC code
2. Performance Baseline: Run analyze_qec_logical_error_rate to establish logical vs physical error rate expectations
3. Circuit Generation: Execute generate_circuits to create multiple circuit variants using RL and classical algorithms

1. Timeline Analysis: Use plot_timeline to analyze execution scheduling and identify bottlenecks
2. Circuit Simplification: Apply simplify_best_circuit to reduce depth and optimize gate count
3. Performance Evaluation: Run compare_circuits_fidelity to rank circuits by simulated performance

1. Benchmark Data Generation: Use simulate_gate_benchmark_data to create standardized calibration data
2. Fidelity Extraction: Run analyze_gate_fidelity_from_data to extract real hardware parameters
3. Calibrated Evaluation: Feed calibrated rates back into circuit evaluation for realistic ranking
4. Iterative Refinement: Use calibrated metrics to refine QEC assumptions and circuit selection

1. Pulse Design: Optimize X and CZ gate pulses using optimize_x_gate and optimize_cz_gate
2. Robustness Analysis: Review pulse shapes, convergence, and robustness metrics
3. Final Instructions: Generate optimized physical control instructions for hardware execution

Requires Python ≥ 3.13 and Julia ≥ 1.11.

# Install Python dependencies
uv sync

# Install Julia dependencies
julia --project=juliamcp -e "using Pkg; Pkg.instantiate()"

The project exposes a console script and a module entrypoint; both start the MCP stdio server and the monitoring GUI:

# Using the console script
uv run mcp-gate-optimize

# Or as a module
uv run python -m gate_optimize

The intelligent monitoring GUI displays:

• Real-time optimization progress and fidelity traces
• Live circuit visualizations and timeline plots
• Hardware calibration results and performance metrics
• Interactive progress tracking for all workflow phases

Add to your MCP client configuration:

{
  "mcpServers": {
    "tensorqec-server": {
      "command": "julia",
      "args": ["--project=/path/to/project/juliamcp", "/path/to/project/juliamcp/server.jl"]
    }
  }
}

uv run pytest -q

• Quantum Computing: Qiskit, Stim, PyMatching
• Machine Learning: PyTorch, SwanLab
• Optimization: SciPy, NumPy
• Visualization: Matplotlib, CairoSVG
• GUI: PyQt6, Flask
• Protocol: MCP (Model Context Protocol)

• QEC: TensorQEC, QECCore
• Protocol: ModelContextProtocol

Suggestions and Comments in the Issues are welcome.