Dissipative learning of a quantum classifier

Korkmaz, Ufuk; TÜRKPENÇE, DENIZ

doi:10.1007/s12043-023-02653-7

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Wang et al propose a classifier that consists in a QuBit of interest that gets influenced by the dissipative dynamics of other auxiliary QuBits dispersed on the environment [14]. Still, on the idea of using quantum dissipative models, Korkmaz et al bring a full study about the learning task on a quantum classifier based on collisions [15]. They show that the model is suitable for gradient descendent training.…”

Section: Introductionmentioning

confidence: 99%

Quantum classifier based on open quantum systems with amplitude information loading

Brito,

Neto,

Bernardes

2024

Preprint

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Although the studies on quantum algorithms have been progressing, it is still necessary to broaden the investigation of open quantum systems. In this study, we present the use of an open quantum system to implement a quantum classifier algorithm. Zhang et al. propose a one QuBit system interacting with the environment through a unitary operator that comes from the Hamiltonian. In our proposal, the input data is loaded into the amplitude of the environment instead of being in the unitary operator. This change positively impacts the performance of different databases tested and causes a difference in the system entanglement behavior. For evaluation, both the Zhang et. al model and the proposed model were tested in four real-world datasets and seven other toy problems. The results are evaluated according to accuracy and F1-Score. A deeper analysis of the Iris dataset is also done, checking the creation of entanglement and an extensive random search for better parameters on the proposed model. The results show that for most real-world datasets configurations, the proposed model, although having a simpler decision area, performed better than the one inspired by the Zhang et. al model, and that there is no pattern for the system entanglement in the Iris Dataset.

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Section: Introductionmentioning

confidence: 99%