Researchers are tackling a key challenge hindering the progress of digital quantum computing: efficiently loading classical data into quantum circuits. Kaining Zhang, Xinbiao Wang, and Yuxuan Du, from Nanyang Technological University, alongside Min-Hsiu Hsieh and Dacheng Tao, present a novel data loading framework, termed AQER, that addresses limitations in existing approximate quantum loading methods. This work is significant because it establishes a unified theoretical understanding of approximation errors in data loading, revealing a direct link between infidelity and entanglement entropy. By systematically reducing entanglement, AQER demonstrably outperforms current techniques in both accuracy and efficiency across diverse datasets, including images, language and many-body quantum states, paving the way for scalable quantum data processing and practical applications.
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