Supervised Quantum Image Processing

2025-07-29

AI Summary

To address efficiency bottlenecks in image storage and processing under big data regimes, this work systematically compares four quantum image representations—FRQI, NEQR, QPIE, and tensor network encoding—in terms of information compression performance, and investigates the accuracy–memory trade-off of quantum kernels in binary classification. Experimental results show that FRQI achieves significantly higher image compression ratios than the other three representations; moreover, quantum kernels attain classification accuracy comparable to classical linear kernels while incurring exponentially lower memory overhead (e.g., *O*(log *N*) versus *O*(*N*)). This study provides the first unified, quantitative evaluation of compression efficacy across major quantum image encodings and empirically validates the practical advantage of quantum kernels in resource-constrained settings. The findings establish actionable algorithm selection guidelines and performance benchmarks for quantum–classical hybrid image processing systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Image and Video Retrieval
• Data Mining & Knowledge Management: Data Compression

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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 performs a higher compression of image information than TNR, NEQR, and QPIE. 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.

Problem

• Comparing compression properties of four Quantum Image Representations
• Evaluating trade-off between accuracy and memory in classification
• Assessing quantum kernels' resource efficiency versus classical methods

Innovation

• Compares four Quantum Image Representations for efficiency
• FRQI achieves higher image compression than others
• Quantum kernels reduce storage needs exponentially