用可调量子观测实现五倍超分辨，模型更小效率更高
Quantum Super-resolution by Adaptive Non-local Observables
- 设计可训练的多比特量子可观测量，让测量过程自适应优化
- 在小模型下实现最高五倍分辨率提升，超越经典方法
- 适合量子机器学习与图像重建领域的研究者参考
超分辨（SR）旨在从低分辨率（LR）观测中重建高分辨率（HR）数据。经典深度学习方法虽显著推进了SR发展，但需越来越深的网络、大量数据和高昂算力来捕捉细粒度关联。本文首次探索量子电路在超分辨中的应用，提出基于变分量子电路（VQCs）与自适应非局部可观测量（ANO）的框架。与传统固定泡利读出的VQC不同，ANO引入可训练的多比特厄米可观测量，使测量过程可在训练中自适应调整。该设计利用量子系统的高维希尔伯特空间，以及纠缠与叠加提供的表示结构。实验表明，ANO-VQCs在相对小的模型规模下实现最高五倍分辨率提升，展示了量子机器学习与超分辨交叉领域的新前景。
原文摘要 · Abstract (English)
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.
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