Quantum classifiers with a trainable kernel
2024-05-29 09:09
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Kernel function plays a crucial role in machine learning algorithms such as classifiers. In this paper, we aim to improve the classification
performance and reduce the reading out burden of quantum classifiers. We devise a universally trainable quantum feature mapping layout to broaden the scope of feature states and avoid the inefficiently straight preparation of quantum superposition states. We also propose an
improved quantum support vector machine that employs partially evenly weighted trial states. In addition, we analyze its error sources and
superiority. As a promotion, we propose a quantum iterative multiclassifier framework for one-versus-one and one-versus-rest approaches.
Finally, we conduct corresponding numerical demonstrations in the qiskit package. The simulation result of trainable quantum feature
mapping shows considerable clustering performance, and the subsequent classification performance is superior to the existing quantum
classifiers in terms of accuracy and distinguishability.
Article: https://doi.org/10.1103/PhysRevApplied.21.054056
