تقرير
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
| العنوان: | Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits |
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| المؤلفون: | Nguyen, Hoang-Quan, Nguyen, Xuan Bac, Chen, Samuel Yen-Chi, Churchill, Hugh, Borys, Nicholas, Khan, Samee U., Luu, Khoa |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.00843 |
| رقم الأكسشن: | edsarx.2406.00843 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |