Quantum classifier based on open quantum systems with amplitude information loading

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 from the Hamiltonian. In our proposal, the input data are 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, the Zhang et al. proposed models 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 dataset 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.