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This repo contains the code and results of QXX and QXX-MLP.

@article{paler2020machine,
  title={Machine learning optimization of quantum circuit layouts},
  author={Paler, Alexandru and Sasu, Lucian M and Florea, Adrian and Andonie, Razvan},
  journal={arXiv preprint arXiv:2007.14608},
  year={2020}
}

A simple usage example can be seen in main.py. The results for benchmarking QXX with arxiv:2002.09783 are below.

benchmark

The general idea of is that the initial placement of the qubits influences to a great extent the total cost of compiling a circuit for NISQ. Thus, it seems reasonable to:

  1. invest more computational power to find an initial placement using a lookahead heuristic that estimates as good as possible the cost of the fully mapped circuit;
  2. after finalising the initial placement, do not invest too much care and computational power to improve the cost of the placement heuristic.

Some concepts used by QXX were presented in a paper about the K7M heuristic

@inproceedings{paler2019influence,
  title={On the influence of initial qubit placement during NISQ circuit compilation},
  author={Paler, Alexandru},
  booktitle={International Workshop on Quantum Technology and Optimization Problems},
  pages={207--217},
  year={2019},
  organization={Springer}
}

PS: QXX has this name for no particular reason. PS: The name of the K7M heuristic is borrowed from the Renault engine.