Publications

How to cite

If you use this software in your work, please cite it using the following reference:

Rosset D., Montealegre-Mora F., Bancal J-D., In: Paranjape M.B., MacKenzie R., Thomova Z., Winternitz P., Witczak-Krempa W. (eds) Quantum Theory and Symmetries. CRM Series in Mathematical Physics. Springer (2021).

Here is a suggested citation:

This problem was solved with the help of RepLAB [1].

For convenience, here is the reference in BiBTeX format:

@conference{Rosset21,
  author       = {Rosset, D. and Montealegre-Mora, F. and Bancal, J-D.},
  title        = {RepLAB: A Computational/Numerical Approach to Representation Theory},
  booktitle    = {Quantum Theory and Symmetries},
  year         = 2021,
  month        = March,
  editor       = {Paranjape, M.B. and MacKenzie, R. and Thomova, Z. and Winternitz, P. and Witczak-Krempa, W.},
  series       = {CRM Series in Mathematical Physics},
  pages        = 643-653,
  organization = {Proceedings of the 11th International Symposium, Montreal, Canada},
  publisher    = {Springer},
  doi          = {10.1007/978-3-030-55777-5_60},
  url          = {https://link.springer.com/chapter/10.1007%2F978-3-030-55777-5_60},
}

Some publications

Below is a selection of scientific articles that use RepLAB. Let us know if you want your paper to be listed here as well!

• A hierarchy of efficient bounds on quantum capacities exploiting symmetry

• Quantum Key Distribution with Few Assumptions

• Certifying Numerical Decompositions of Compact Group Representations

• Bounding and simulating contextual correlations in quantum theory

• Mutually unbiased bases and symmetric informationally complete measurements in Bell experiments: Bell inequalities, device-independent certification and applications

• RepLAB: a computational/numerical approach to representation theory

The development of RepLAB contributed to the following publication:

• Splitting fields of real irreducible representations of finite groups

RepLAB is also cited in:

• Mutually unbiased bases: polynomial optimization and symmetry

• Universal Approximation Under Constraints is Possible with Transformers