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.

翻译：超分辨率（SR）旨在从低分辨率（LR）观测数据中重建高分辨率（HR）数据。经典的深度学习方法已显著推进了SR的发展，但为了捕捉细粒度的相关性，需要越来越深的网络、庞大的数据集和繁重的计算。在本工作中，我们提出了**首个**研究量子电路用于SR的探索。我们提出了一种基于变分量子电路（VQCs）的框架，并引入了**自适应非局域可观测量**（ANO）测量。与使用固定泡利读出算符的传统VQCs不同，ANO引入了可训练的多量子比特厄米可观测量，使得测量过程能够在训练期间自适应调整。该设计利用了量子系统的高维希尔伯特空间，以及由纠缠和叠加提供的表示结构。实验表明，ANO-VQCs在相对较小的模型规模下，实现了高达五倍的分辨率提升，为量子机器学习与超分辨率的交叉领域指出了一个充满前景的新方向。