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Optimization algorithm for the creation of quantum entanglement purification (distillation) circuits.
Python Julia
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Stefan Krastanov
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Optimized Quantum Entanglement provides a small python library for the creation and optimization of quantum entanglement purification circuits.

Clifford.jl is a higly-optimized julia library for enumerating and studying the Clifford group of multiple qubits.

examples contains multiple notebooks showcasing the use of these libraries. Some of these files were used in the writing of the related paper.

  • Example shows how to run a simple optimization for the creation of purification circuits.

  • Compare_Regimes shows that for different parameter regimes (i.e. error models) different circuits are better.

  • HotCold shows how to augment the library to work with custom error models and hardware architectures. In this case we optimize for a register that has only one "hot" qubit (a communication qubit capable of establishing initial remote entanglement).

  • OptimizeHashingYield shows how to optimize for the hashing yield (defined only for perfect local operations). It is of great theoretical interest in the study of assymptotic circuits, but it is less useful in our case of small circuits optimized for operational errors.

  • Structure_ParallelNaiveCoarseDividing and julia-subgroup are used to enumerate and study the group structure of the Clifford/Permutation operations used in the purification circuits.

See for visualizations and comparisons of circuits generated by this software.

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