2018/03/05 by Changpeng Shao, Shao, Changpeng · 1 citation
Computer Science · #46N50 #FOS: Physical sciences #Machine Learning and ELM #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.1803.01486
openalex publication_date 2018/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
HHL quantum algorithm to solve linear systems is one of the most important subroutines in many quantum machine learning algorithms. In this work, we present and analyze several other caveats in HHL algorithm, which have been ignored in the past. Their influences on the efficiency, accuracy and practicability of HHL algorithm and several related quantum machine learning algorithms will be discussed. We also found that these caveats affect HHL algorithm much deeper than the already noticed caveats. In order to obtain more practical quantum machine learning algorithms with less assumptions based on HHL algorithm, we should pay more attention to these caveats.