  Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading
strategy to utilize near-term quantum advantages in multiple problems,
including machine learning and combinatorial optimization. When applied to
specific tasks, the parameters in the quantum circuits are trained to minimize
the target function. Although there have been comprehensive studies to improve
the performance of the PQCs on practical tasks, the errors caused by the
quantum noise downgrade the performance when running on real quantum computers.
In particular, when the quantum state is transformed through multiple quantum
circuit layers, the effect of the quantum noise happens cumulatively and
becomes closer to the maximally mixed state or complete noise. This paper
studies the relationship between the quantum noise and the diffusion model.
Then, we propose a novel diffusion-inspired learning approach to mitigate the
quantum noise in the PQCs and reduce the error for specific tasks. Through our
experiments, we illustrate the efficiency of the learning strategy and achieve
state-of-the-art performance on classification tasks in the quantum noise
scenarios.
