对比四种量子图像表示法，发现FRQI和QPIE压缩效果更优。
Analysis of Quantum Image Representations for Supervised Classification
- 比较TNR、FRQI、NEQR、QPIE四类量子图像编码的压缩性能。
- FRQI与QPIE在图像压缩上优于TNR和NEQR，且分类准确率相当。
- 适合对量子图像处理与高效存储感兴趣的科研人员。
在大数据与人工智能时代，数据量增长和复杂计算需求推动了数据存储、处理与分析效率的提升。量子图像处理（QIP）作为量子信息科学与图像处理的交叉领域，有望借助量子计算优势缓解这些挑战。本文对比分析了四种量子图像表示法（QImRs）：张量网络表示（TNR）、灵活量子图像表示（FRQI）、新型增强量子表示（NEQR）和量子概率图像编码（QPIE）的压缩特性。仿真结果显示，FRQI与QPIE在图像信息压缩方面优于TNR与NEQR。此外，我们研究了二分类任务中精度与内存之间的权衡，评估基于QImRs的量子核方法与经典线性核的性能。结果表明，量子核可实现相当的平均分类准确率，但图像存储所需资源呈指数级减少。
原文摘要 · Abstract (English)
In the era of big data and artificial intelligence, the increasing volume of data and the demand to solve more and more complex computational challenges are two driving forces for improving the efficiency of data storage, processing and analysis. Quantum image processing (QIP) is an interdisciplinary field between quantum information science and image processing, which has the potential to alleviate some of these challenges by leveraging the power of quantum computing. In this work, we compare and examine the compression properties of four different Quantum Image Representations (QImRs): namely, Tensor Network Representation (TNR), Flexible Representation of Quantum Image (FRQI), Novel Enhanced Quantum Representation NEQR, and Quantum Probability Image Encoding (QPIE). Our simulations show that FRQI and QPIE perform a higher compression of image information than TNR and NEQR. Furthermore, we investigate the trade-off between accuracy and memory in binary classification problems, evaluating the performance of quantum kernels based on QImRs compared to the classical linear kernel. Our results indicate that quantum kernels provide comparable classification average accuracy but require exponentially fewer resources for image storage.
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