symqnet-molopt 3.5.4

SymQNet Molecular Optimization via Hamiltonian Estimation

SymQNet-MolOpt: Hamiltonian Parameter Estimation

SymQNet-MolOpt provides efficient, uncertainty-aware estimation of Hamiltonian parameters for 1D and 2D molecular models and ultimately much more efficient molecular optimization. It is designed for sample-efficient optimization and reports confidence intervals for each parameter.

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Installation

pip install SymQNet-MolOpt

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Usage

Core Command

SymQNet-MolOpt --hamiltonian input.json --output results.json

Arguments:

• --hamiltonian: Path to a JSON Hamiltonian (OpenFermion-like format).
• --output: File to save results (JSON).
• --shots: Number of measurement shots (default auto-scales).
• --n-rollouts: Number of independent rollouts (default: 5).
• --max-steps: Max optimization steps per rollout (default: 50).

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Examples

Water Molecule (H₂O, 10 qubits)

SymQNet-MolOpt --hamiltonian examples/H2O_10q.json --output h2o_results.json --shots 1024 --n-rollouts 5 --max-steps 50

Ising Chain (12 qubits)

SymQNet-MolOpt --hamiltonian examples/ising_12q.json --output ising_results.json --shots 1024

(You need to create your own JSON Hamiltonian.)

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Input Format

Hamiltonians are specified in JSON:

{
  "format": "openfermion",
  "system": "H2O",
  "n_qubits": 10,
  "pauli_terms": [
    {"coefficient": -74.943, "pauli_string": "IIIIIIIIII"},
    {"coefficient": 0.342, "pauli_string": "IIIIIIIIIZ"}
  ]
}

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Output Format

Results include estimated parameters with uncertainties:

{
  "symqnet_results": {
    "coupling_parameters": [
      {
        "index": 0,
        "mean": 0.2134,
        "confidence_interval": [0.2089, 0.2179],
        "uncertainty": 0.0045
      }
    ],
    "field_parameters": [...],
    "total_uncertainty": 0.0856
  },
  "hamiltonian_info": {
    "system": "H2O",
    "n_qubits": 10
  }
}

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Requirements

• Python 3.8+
• PyTorch 1.12+
• NumPy, SciPy, Click