Abstract
Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but require increasingly deeper networks, large datasets, and heavy computation to capture fine-grained correlations. In this work, we present the \emph{first study} to investigate quantum circuits for SR. We propose a framework based on Variational Quantum Circuits (VQCs) with \emph{Adaptive Non-Local Observable} (ANO) measurements. Unlike conventional VQCs with fixed Pauli readouts, ANO introduces trainable multi-qubit Hermitian observables, allowing the measurement process to adapt during training. This design leverages the high-dimensional Hilbert space of quantum systems and the representational structure provided by entanglement and superposition. Experiments demonstrate that ANO-VQCs achieve up to five-fold higher resolution with a relatively small model size, suggesting a promising new direction at the intersection of quantum machine learning and super-resolution.
Abstract (translated)
超分辨率（SR）的目标是从低分辨率（LR）的观测数据中重建高分辨率（HR）的数据。传统的深度学习方法已经大大推进了超分辨率技术的发展，但这些方法需要越来越深的网络、大型数据集以及大量的计算资源来捕捉细微的相关性。在这项工作中，我们首次研究了量子电路在超分辨率中的应用。我们提出了一个基于变分量子线路（VQCs）并采用自适应非局部可观测量（ANO）的框架。 与传统的具有固定泡利读出操作的VQC不同，ANO引入了可训练的多量子比特厄米算子，使得测量过程可以在训练过程中进行调整和优化。这种设计利用了量子系统的高维希尔伯特空间以及纠缠和叠加提供的表示结构。实验结果表明，ANO-VQCs在模型规模相对较小的情况下可以实现高达五倍分辨率的提升，这表明在量子机器学习与超分辨率技术交叉领域中存在一个有前景的新发展方向。
URL
https://arxiv.org/abs/2601.14433
