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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Metaheuristic optimization scheme for quantum kernel classifiers using entanglement-directed graphs

  • Yozef Tjandra (Calvin Institute of Technology) ;
  • Hendrik Santoso Sugiarto (Calvin Institute of Technology)
  • Received : 2024.04.03
  • Accepted : 2024.08.13
  • Published : 2024.10.10
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Abstract

Entanglement is crucial for achieving quantum advantages. However, in the context of quantum machine learning, existing optimization strategies for generating quantum classifier circuits often result in unentangled circuits, indicating an underutilization of the entanglement effect needed to learn complex patterns. In this study, we proposed a novel metaheuristic approach-genetic algorithm-for designing a quantum kernel classifier that incorporates expressive entanglement. This classifier utilizes a loopless entanglement-directed graph, where each directed edge represents the entanglement between the target and control qubits. The proposed method consistently outperforms classical and quantum baselines across various artificial and actual datasets, achieving improvements up to 32.4% and 17.5%, respectively, compared with the best model among all other baselines. Moreover, this method successfully reconstructs the hidden entanglement structures underlying artificial datasets. The results also demonstrate that the optimized circuits exhibit diverse entanglement variations across different datasets, indicating the versatility of the proposed approach.

Keywords

Acknowledgement

This work was funded by PT Lancs Arche Consumma (MOU.003/CIT/XI/2022) and PT Astra International TBK - TSO (MOU.002/CIT/XI/2022).

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