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MCP AI Integration

Tested with: Quanta SDK v1.2.0-production

What You'll Learn

Connect Quanta to Claude, GPT, and other AI assistants using the Model Context Protocol (MCP).

Prerequisites

What is MCP?

MCP (Model Context Protocol) lets AI assistants discover and call tools directly. Quanta exposes 23 native quantum tools that any MCP client can use:

Tool Category Description
run_circuit Core Circuit Build and simulate any quantum circuit
create_bell_state Core Circuit Instant Bell state preparation
list_gates Core Circuit Inspect all 31 native quantum gates
draw_circuit Core Circuit Visual ASCII / SVG circuit diagram
optimize_circuit Compilation Multi-pass compiler optimization
transpile_for_target Compilation Target architecture transpilation
grover_search Algorithms Quadratic search algorithm
shor_factor Algorithms Integer factorization via period finding
qaoa_optimize Algorithms Combinatorial QAOA solver
monte_carlo_price Finance Amplitude estimation option pricing
option_greeks Finance Quantum Monte Carlo Greeks evaluation
cluster_data Machine Learning Swap-test quantum k-means clustering
qml_classify Machine Learning Differentiable quantum classification
simulate_noise Noise & Systems 7-channel Kraus open system simulation
surface_code_simulate 2026 FTQC Stabilizer surface code simulation
compare_decoders 2026 FTQC Benchmark Edmonds Blossom vs heuristics
qec_diagnose 2026 FTQC Error syndrome diagnosis and extraction
estimate_fault_tolerant_cost 2026 FTQC Physical qubit overhead estimation
quanta_reasoning_eval Cognitive Biomorphic Zeno cognitive decision arbitration
explain_result AI Utilities AI-native structured measurement analysis
run_on_ibm Hardware Submit job to IBM Quantum cloud
ibm_backends Hardware Enumerate real quantum processors
ibm_job_result Hardware Poll remote quantum hardware execution

Setup — Claude Desktop

Install the MCP server locally:

# Install Quanta and fastmcp
pip install quanta-sdk fastmcp

# Register with Claude Desktop
fastmcp install quanta/mcp_server.py --name "Quanta Quantum SDK"

Then in Claude Desktop, you can say:

"Create a Bell state and explain the results"

Claude will call create_bell_state → explain_result automatically.

Setup — Cloud Deployment

For always-on MCP access:

# Run as SSE server (Cloud Run, Lambda)
python -m quanta.mcp_server --transport sse --port 8080

Example AI Conversations

"Factor the number 15"

→ AI calls shor_factor(N=15) → Returns {factors: [3, 5]}

"Search for value 7 in a 4-qubit space"

→ AI calls grover_search(target=7, num_bits=4) → Returns results

"Price a European call option with spot=100, strike=105"

→ AI calls monte_carlo_price(...) → Returns quantum vs classical prices

"Show me the noise effect on a Bell state at 5% error"

→ AI calls simulate_noise(circuit="bell", error_rate=0.05) → Returns fidelity

SDK Info via MCP

# The MCP server exposes SDK metadata
import json
sdk_info = {
    "name": "quanta-sdk",
    "version": "0.8.1",
    "tools": 14,
    "gates": 25,
    "algorithms": 12,
}
print(json.dumps(sdk_info, indent=2))

Try It Yourself

  1. Install the MCP server in Claude Desktop and ask it to explain quantum entanglement with a live Bell state
  2. Ask Claude to compare noisy vs ideal simulation
  3. Try "Factor 21 using Shor's algorithm" in conversation

Learning Path Complete! 🎉

You've completed all 8 tutorials:

Tutorial Topic
01 Getting Started — first circuit
02 Gates and Circuits — all 31 gates
03 Simulation — noise and fidelity
04 Algorithms — Grover, QAOA, VQE, Shor
05 IBM Hardware — real quantum computers
06 QML — quantum machine learning
07 QEC — error correction
08 MCP — AI integration

Next steps: - Cookbook — Copy-paste recipes for common tasks - Migration Guide — Coming from Qiskit? - GitHub — Contribute!