Psi-MCP by manasp21 - MCP Server

Psi-MCP: Advanced Quantum Systems MCP Server

The most comprehensive quantum physics MCP server for complex open and closed quantum systems calculations

🌟 Overview

Psi-MCP is an advanced Model Context Protocol (MCP) server specifically designed for quantum systems analysis and simulation. It provides comprehensive tools for quantum computing, quantum chemistry, many-body physics, quantum machine learning, and quantum field theory calculations.

Key Features

🔬 Quantum Circuit Operations: Create, simulate, optimize, and visualize quantum circuits
⚛️ Open Quantum Systems: Solve master equations, analyze decoherence, compute steady states
🧪 Quantum Chemistry: Molecular Hamiltonians, VQE, electronic structure calculations
🔗 Many-Body Physics: DMRG, tensor networks, phase transitions, correlation functions
🤖 Quantum Machine Learning: QNNs, variational classifiers, quantum kernels
🌊 Quantum Field Theory: Field quantization, path integrals, RG flow, anomalies
📊 Advanced Visualization: Bloch spheres, density matrices, Wigner functions
🚀 Smithery Compatible: Easy deployment and integration

🛠 Installation

Prerequisites

Python 3.11 or higher
Docker (for containerized deployment)
Git

Core vs Optional Dependencies

Core Dependencies (always installed):

FastAPI, Uvicorn (MCP server framework)
Qiskit, Cirq, PennyLane (quantum computing)
QuTiP (open quantum systems)
OpenFermion (quantum chemistry)
NumPy, SciPy, Matplotlib (numerical computing)

Optional Dependencies (install separately if needed):

PySCF (advanced quantum chemistry)
TensorFlow Quantum (quantum ML)
NetKet (neural quantum states)
Additional quantum libraries

Quick Start with Smithery

# Install via Smithery CLI
npx @smithery/cli install psi-mcp --client cursor

# Or deploy via GitHub integration
git clone https://github.com/manasp21/Psi-MCP.git
cd Psi-MCP
# Push to your GitHub repository and connect to Smithery

Local Development

# Clone the repository
git clone https://github.com/manasp21/Psi-MCP.git
cd Psi-MCP

# Install dependencies
pip install -r requirements.txt

# Run the server
python src/server.py

Docker Deployment

# Build the container
docker build -t psi-mcp .

# Run with configuration
docker run -p 8000:8000 \
  -e computing_backend=simulator \
  -e max_qubits=20 \
  -e precision=double \
  psi-mcp

🔧 Configuration

Smithery Configuration

Configure via the Smithery dashboard or query parameters:

computing_backend: "simulator"  # qasm_simulator, statevector_simulator
max_qubits: 20                  # Maximum qubits (1-30)
precision: "double"             # single, double, extended
enable_gpu: false               # GPU acceleration
timeout_seconds: 300            # Calculation timeout
memory_limit_gb: 4              # Memory limit

Environment Variables

PORT=8000                       # Server port
HOST=0.0.0.0                   # Server host
COMPUTING_BACKEND=simulator     # Default backend
MAX_QUBITS=20                  # Default max qubits

🚀 Usage

Quantum Circuit Operations

Create Quantum Circuits

# Create a Bell state circuit
create_quantum_circuit(
    num_qubits=2,
    circuit_type="bell",
    backend="qasm_simulator"
)

# Create a GHZ state
create_quantum_circuit(
    num_qubits=4,
    circuit_type="ghz",
    backend="statevector_simulator"
)

# Create quantum Fourier transform
create_quantum_circuit(
    num_qubits=3,
    circuit_type="qft",
    backend="simulator"
)

Simulate Circuits

# Simulate with measurements
simulate_quantum_circuit(
    circuit_definition="circuit_1",
    shots=1024,
    backend="qasm_simulator"
)

# Get statevector
simulate_quantum_circuit(
    circuit_definition="circuit_2",
    shots=1,
    backend="statevector_simulator"
)

Optimize Circuits

# Optimize for specific backend
optimize_quantum_circuit(
    circuit_definition="circuit_1",
    optimization_level=2,
    target_backend="qasm_simulator"
)

Open Quantum Systems

Master Equation Solving

# Solve Lindblad master equation
solve_master_equation(
    hamiltonian="pauli_z",
    collapse_operators="spontaneous_emission",
    initial_state="excited",
    time_span="0,10,100",
    solver_method="mesolve"
)

# Analyze decoherence
analyze_decoherence(
    system_hamiltonian="pauli_x",
    environment_coupling="dephasing",
    temperature=0.1,
    analysis_type="dephasing"
)

Quantum Chemistry

Molecular Calculations

# Generate molecular Hamiltonian
generate_molecular_hamiltonian(
    molecule="H2",
    basis="sto-3g",
    charge=0,
    multiplicity=1
)

# Run VQE for electronic structure
vqe_chemistry(
    molecule="H2O",
    basis="6-31g",
    ansatz="uccsd",
    optimizer="cobyla"
)

# Simulate chemical reactions
simulate_chemical_reaction(
    reactants=["H2", "O2"],
    products=["H2O"],
    method="vqe"
)

Quantum Algorithms

Shor's Algorithm

# Factor integers
shors_algorithm(
    N=15,
    backend="qasm_simulator",
    shots=1024
)

Grover's Search

# Search marked items
grovers_search(
    marked_items=[3, 7],
    search_space_size=16,
    backend="simulator"
)

VQE Optimization

# Variational quantum eigensolver
vqe_optimization(
    hamiltonian="ising",
    ansatz_type="ry",
    optimizer="cobyla",
    max_iterations=100
)

Many-Body Physics

DMRG Simulations

# Run DMRG for spin chains
dmrg_simulation(
    hamiltonian_type="heisenberg",
    system_size=20,
    bond_dimension=100,
    max_sweeps=10
)

# Phase transition analysis
phase_transition_analysis(
    model_type="ising",
    parameter_range=[0.0, 2.0],
    n_points=20,
    system_size=16
)

Quantum Machine Learning

Neural Networks

# Train quantum neural network
quantum_neural_network(
    input_data=[[0.1, 0.2], [0.3, 0.4]],
    labels=[0, 1],
    n_qubits=4,
    n_layers=2,
    epochs=50
)

# Variational classifier
variational_classifier(
    training_data=train_X,
    training_labels=train_y,
    test_data=test_X,
    ansatz_type="hardware_efficient"
)

Visualization

Quantum States

# Bloch sphere visualization
visualize_quantum_state(
    state_definition="superposition",
    visualization_type="bloch_sphere"
)

# Density matrix plot
visualize_quantum_state(
    state_definition="bell",
    visualization_type="density_matrix"
)

# Wigner function
visualize_quantum_state(
    state_definition="coherent",
    visualization_type="wigner_function"
)

📚 API Reference

Core Tools

Tool Name	Description	Parameters
`create_quantum_circuit`	Create quantum circuits	`num_qubits`, `circuit_type`, `backend`
`simulate_quantum_circuit`	Simulate circuits	`circuit_definition`, `shots`, `backend`
`solve_master_equation`	Solve open system dynamics	`hamiltonian`, `collapse_operators`, `initial_state`
`vqe_optimization`	Variational quantum eigensolver	`hamiltonian`, `ansatz_type`, `optimizer`
`dmrg_simulation`	Many-body simulations	`hamiltonian_type`, `system_size`, `bond_dimension`
`quantum_neural_network`	Train QNNs	`input_data`, `labels`, `n_qubits`, `n_layers`

Supported Backends

Qiskit: qasm_simulator, statevector_simulator, unitary_simulator
Cirq: cirq_simulator
PennyLane: default.qubit, default.qubit.torch

Circuit Types

empty: Empty circuit
bell: Bell state preparation
ghz: GHZ state preparation
qft: Quantum Fourier transform
random: Random circuit

🏗 Architecture

Psi-MCP/
├── src/
│   ├── server.py              # Main MCP server
│   └── quantum/               # Quantum modules
│       ├── __init__.py        # Backend initialization
│       ├── circuits.py        # Circuit operations
│       ├── systems.py         # Open quantum systems
│       ├── algorithms.py      # Quantum algorithms
│       ├── chemistry.py       # Quantum chemistry
│       ├── many_body.py       # Many-body physics
│       ├── field_theory.py    # Quantum field theory
│       ├── ml.py             # Quantum ML
│       ├── visualization.py   # Visualization tools
│       └── utils.py          # Utility functions
├── tests/                     # Test suite
├── docs/                      # Documentation
├── smithery.yaml             # Smithery configuration
├── Dockerfile               # Container configuration
├── requirements.txt         # Python dependencies
└── README.md               # This file

🧪 Examples

Complete Workflow Example

# 1. Create and simulate a quantum circuit
circuit_result = create_quantum_circuit(
    num_qubits=3,
    circuit_type="ghz",
    backend="qasm_simulator"
)

# 2. Simulate the circuit
simulation_result = simulate_quantum_circuit(
    circuit_definition=circuit_result['id'],
    shots=1000,
    backend="qasm_simulator"
)

# 3. Visualize the results
plot_result = plot_measurement_results(
    counts=simulation_result['counts'],
    title="GHZ State Measurement"
)

# 4. Analyze entanglement
entropy = calculate_entanglement_entropy(
    circuit_definition=circuit_result['id'],
    subsystem_size=1
)

Quantum Chemistry Workflow

# 1. Generate molecular Hamiltonian
hamiltonian = generate_molecular_hamiltonian(
    molecule="H2",
    basis="sto-3g"
)

# 2. Run VQE calculation
vqe_result = vqe_chemistry(
    molecule="H2",
    basis="sto-3g",
    ansatz="uccsd"
)

# 3. Compute molecular properties
properties = compute_molecular_properties(
    molecule="H2",
    method="hf",
    basis="sto-3g"
)

🔬 Advanced Features

Custom Hamiltonians

# Define custom spin chain
solve_master_equation(
    hamiltonian=json.dumps([[1, 0], [0, -1]]),
    collapse_operators="custom_operators",
    initial_state="custom_state"
)

GPU Acceleration

# Enable GPU support
configure_server(
    enable_gpu=True,
    computing_backend="gpu_simulator"
)

Parallel Processing

# Parallel circuit simulation
simulate_circuits_parallel(
    circuit_definitions=["circuit_1", "circuit_2", "circuit_3"],
    shots=1000
)

📊 Performance

Benchmarks

Operation	System Size	Execution Time	Memory Usage
Circuit Simulation	20 qubits	~2s	~100MB
VQE Optimization	H2O molecule	~30s	~200MB
DMRG Calculation	50 sites	~60s	~500MB
QNN Training	100 samples	~45s	~150MB

Scaling

Quantum Circuits: Up to 30 qubits (simulator dependent)
Many-Body Systems: Up to 100 sites with DMRG
Molecular Systems: Up to 20 orbitals with VQE
ML Training: Up to 1000 samples efficiently

🤝 Contributing

We welcome contributions! Please see our for details.

Development Setup

# Clone and install development dependencies
git clone https://github.com/manasp21/Psi-MCP.git
cd Psi-MCP
pip install -r requirements.txt
pip install -e .[dev]

# Run tests
pytest tests/

# Format code
black src/ tests/
isort src/ tests/

# Type checking
mypy src/

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

Qiskit Team for quantum computing framework
QuTiP Developers for open quantum systems tools
PennyLane Team for quantum machine learning
OpenFermion Contributors for quantum chemistry tools
Smithery Platform for MCP server hosting

📞 Support

GitHub Issues: Report bugs or request features
Documentation: Full API documentation
Examples: Jupyter notebooks with examples

🗺 Roadmap

v1.1.0 (Next Release)

ITensor integration for tensor networks
NetKet support for neural quantum states
Advanced error mitigation tools
Quantum error correction codes

v1.2.0 (Future)

Hardware backend support (IBM, Google, IonQ)
Advanced visualization dashboard
Quantum advantage benchmarks
Multi-user collaboration features

Built with ❤️ for the quantum computing community

🌟 Star us on GitHub | 📖 Read the Docs | 🚀 Deploy on Smithery