Quantum semi-supervised generative adversarial network for enhanced data classification

Quantum semi-supervised generative adversarial network for enhanced data classification

Blog Article

Abstract In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN).The system is composed Car Lights of a quantum generator and a classical discriminator/classifier (D/C).The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset.Hence the qSGAN needs neither any data loading nor to generate a pure quantum state, implying that qSGAN is much easier to implement than many existing quantum algorithms.Also the generator can serve as a stronger adversary than a classical one thanks to its rich expressibility, and it is expected to be robust against noise.

These advantages are demonstrated in a Neck Serum numerical simulation.