CUDANT

Fidelity-gated GPU quantum & physics simulator with MCP agent interface.

CUDANT gives an LLM agent direct access to a GPU quantum simulator that refuses to answer when it cannot be trusted. No silently wrong results. No hallucinated qubits. If the bond dimension is too small, it tells you. If the gate syntax is ambiguous, it tells you. You get a result or you get a reason.

What it does

Related MCP server: genpark-quantum-qubit-state-vector-simulator-skill

The fidelity gate

run_mps runs every circuit TWICE (at chi_max and chi_max x 4). If the top-1 probability differs by more than epsilon, it REFUSES and returns advice on how to fix it. The LLM reads the advice, retries, and gets a confirmed answer. No human in the loop.

Requirements

• NVIDIA GPU (Pascal or newer, 4GB+ VRAM)

• Windows 11 + WSL2 (or native Linux)

• Python 3.10+

Install

WSL2 (Windows)

wsl --install -d Ubuntu-24.04

Reboot. Inside Ubuntu:

sudo apt update
sudo apt install -y python3-pip python3.12-venv
python3 -m venv ~/cudant
source ~/cudant/bin/activate
pip install -r requirements.txt

Verify GPU

nvidia-smi

Should show your GPU. If not, install the latest NVIDIA Windows driver.

Run the server

python3 cudant_mcp.py

You should see the FastMCP banner. The server is now listening on stdio.

Connect to an LLM (Pi)

In ~/.pi/agent/mcp.json:

{
  "mcpServers": {
    "cudant": {
      "command": "wsl",
      "args": ["-d", "Ubuntu-24.04", "--", "/home/USER/cudant/bin/python", "/home/USER/cudant_mcp.py"],
      "exposure": "direct",
      "description": "GPU quantum/physics simulator. Tools: run_lion (exact, 2-16 qubits), run_mps (fidelity-gated, 17-128 qubits), settle_field (3D GPU field), widen_search (batch Grover)."
    }
  }
}

Replace USER with your username. Restart your LLM client.

Gate syntax

H0  X1  Z2          Single-qubit (letter + index)
CNOT01              CNOT qubits 0-9 (no underscore)
CNOT9_10            CNOT qubits 10+ (underscore REQUIRED)
CNOT10_11           CNOT qubits 10+

Gates are comma-separated: "H0,CNOT01,CNOT1_2,CNOT9_10"

Example prompts

Architecture

LLM Agent (Pi, Claude, etc.)
    |
    |  MCP (stdio, JSON-RPC)
    v
cudant_mcp.py  (FastMCP server)
    |
    +-- run_lion ----> PyTorch CUDA (dense, exact)
    +-- run_mps -----> CUDA-Q tensornet-mps (MPS, fidelity-gated)
    +-- settle_field-> PyTorch conv3d (3D field)
    +-- widen_search-> PyTorch batched Grover

Tested

Hardware: Quadro RTX 4000 (8GB VRAM), i9-9880H, WSL2 on Windows 11.

License

MIT