Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits

TL;DR

This paper introduces a diffusion-inspired method to mitigate quantum noise in parameterized quantum circuits, significantly improving their performance on classification tasks under realistic noisy conditions.

Contribution

The paper proposes a novel diffusion-inspired learning approach that effectively reduces quantum noise effects in PQCs, enhancing their practical utility.

Findings

Achieves state-of-the-art classification accuracy under quantum noise

Demonstrates the effectiveness of diffusion-inspired noise mitigation

Reduces error rates in noisy quantum circuit simulations

Abstract

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.