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Preprint
Report number	arXiv:2411.13468
Title	Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks
Author(s)	Khoo, Jun Yong (A-STAR, Singapore) ; Gan, Chee Kwan (A-STAR, Singapore) ; Ding, Wenjun (A-STAR, Singapore) ; Carrazza, Stefano (CERN ; Milan U. ; INFN, Milan) ; Ye, Jun (A-STAR, Singapore ; Technol. Innovation Inst., UAE) ; Kong, Jian Feng (A-STAR, Singapore)
Document contact	Contact: arXiv
Imprint	2024-11-20
Number of pages	3
Note	3 pages, 2 figures
Subject category	quant-ph ; General Theoretical Physics
Abstract	Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient ansatz (HEA) for classifying the phases of quantum ground states of the transverse field Ising model and the XXZ model. Various system sizes, including 4, 8, and 16 qubits, through simulation were examined. Additionally, QCNN and HEA-based autoencoders were implemented to assess their capabilities in compressing quantum states. The results show that QCNN with RY gates can be trained faster due to fewer trainable parameters while matching the performance of HEAs.
Other source	Inspire
Copyright/License	preprint: (License: CC BY 4.0)