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RDOperator

The RDOperator class is a symbolic representation of a quantum operator with an associated finite dimensional matrix and subspace. It extends the Operator class and provides essential functionalities for symbolic computation and representation of quantum mechanical operators.

Parameters:

  • name (str):
    • The unique identifier for the operator. This parameter can accept LaTeX encodings for symbolic printing.
  • matrix (Matrix):
    • The matrix representation for the given operator.
  • subspace (str, optional):
    • Indicates the subspace where the operator acts. If not provided, it defaults to 'default'.

Attributes

  • name (str):
    • The unique identifier for the operator.
  • matrix (Matrix):
    • The matrix representation for the given operator.
  • subspace (str):
    • Indicates the Hilbert subspace on which the operator acts.

Usage Example

Below is a practical example that demonstrates how to create an RDOperator instance and utilize its properties and methods.

from sympt import RDOperator
from sympy import Matrix, eye, sympify

# Define a simple 2x2 identity matrix
name = "Omega_0"
mat = Matrix([[1, 0], [0, 1]])
subspace = "Omega"

# Create the RDOperator instance
operator = RDOperator(name, mat, subspace)

# Accessing attributes
display(operator)
print("Operator Name:", operator.name)
print("Operator Matrix:\n", operator.matrix)
print("Operator Subspace:", operator.subspace)

\(\Omega_0\)

Operator Name: Omega_0
Operator Matrix:
 Matrix([[1, 0], [0, 1]])
Operator Subspace: Omega

License

SymPT is licensed under the MIT License. See the LICENSE file for details.

Citation

If you use SymPT in your research, please cite the following paper:

BibTeX Entry:

@misc{diotallevi2024symptcomprehensivetoolautomating,
      title={SymPT: a comprehensive tool for automating effective Hamiltonian derivations}, 
      author={Giovanni Francesco Diotallevi and Leander Reascos and Mónica Benito},
      year={2024},
      eprint={2412.10240},
      archivePrefix={arXiv},
      primaryClass={quant-ph},
      url={https://arxiv.org/abs/2412.10240}, 
}

APA Citation:

Diotallevi, G. F., Reascos, L., & Benito, M. (2024). SymPT: a comprehensive tool for automating effective Hamiltonian derivations. arXiv preprint arXiv:2412.10240.

IEEE Citation:

G. F. Diotallevi, L. Reascos, and M. Benito, "SymPT: a comprehensive tool for automating effective Hamiltonian derivations," arXiv preprint arXiv:2412.10240, 2024.

References