Adiabatic Quantum Computing for Max-Sum Diversication
The combinatorial problem of max-sum diversication asks for a maximally diverse subset of a given set of data. Here, we show that it can be expressed as an Ising energy minimization problem. Given this result, maxsum diversication can be solved on adiabatic quantum computers and we present proof of concept simulations which support this claim. This, in turn, suggests that quantum computing might play a role in data mining. We therefore discuss quantum computing in a tutorial like manner and elaborate on its current strengths and weaknesses for data analysis.
- Published in:
Proceedings of the 2020 SIAM International Conference on Data Mining (SDM) SIAM International Conference on Data Mining (SDM) - Type:
Inproceedings - Authors:
C. Bauckhage, R. Sifa, S. Wrobel - Year:
2020
Citation information
C. Bauckhage, R. Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversication, SIAM International Conference on Data Mining (SDM), Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), 2020, https://doi.org/10.1137/1.9781611976236.39, Bauckhage.etal.2020a,
@Inproceedings{Bauckhage.etal.2020a,
author={C. Bauckhage, R. Sifa, S. Wrobel},
title={Adiabatic Quantum Computing for Max-Sum Diversication},
booktitle={SIAM International Conference on Data Mining (SDM)},
journal={Proceedings of the 2020 SIAM International Conference on Data Mining (SDM)},
url={https://doi.org/10.1137/1.9781611976236.39},
year={2020},
abstract={The combinatorial problem of max-sum diversication asks for a maximally diverse subset of a given set of data. Here, we show that it can be expressed as an Ising energy minimization problem. Given this result, maxsum diversication can be solved on adiabatic quantum computers and we present proof of concept simulations which support this claim. This, in turn, suggests that quantum computing might...}}