Quantum learning advantage on a scalable photonic platform

• 成果类型: Article
• 署名作者: Liu, Zheng-Hao; Brunel, Romain; Ostergaard, Emil E. B.; Cordero, Oscar; Chen, Senrui; Wong, Yat; Nielsen, Jens A. H.; Bregnsbo, Axel B.; Zhou, Sisi; Huang, Hsin-Yuan; Oh, Changhun; Jiang, Liang; Preskill, John; Neergaard-Nielsen, Jonas S.; Andersen, Ulrik L.
• 署名单位: Technical University of Denmark; University of Chicago; Perimeter Institute for Theoretical Physics; University of Waterloo; University of Waterloo; University of Waterloo; Alphabet Inc.; Google Incorporated; California Institute of Technology; Massachusetts Institute of Technology (MIT); Korea Advanced Institute of Science & Technology (KAIST)
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-9095
• DOI: 10.1126/science.adv2560
• 发表日期: 2025-09-25
• 页码: 1332-1335
• 关键词: computational advantage

摘要

Recent advances in quantum technologies have demonstrated that quantum systems can outperform classical ones in specific tasks, a concept known as quantum advantage. Although previous efforts have focused on computational speedups, a definitive and provable quantum advantage that is unattainable by any classical system has remained elusive. In this work, we demonstrate a provable photonic quantum advantage by implementing a quantum-enhanced protocol for learning a high-dimensional physical process. Using imperfect Einstein-Podolsky-Rosen entanglement, we achieve a sample complexity reduction of 11.8 orders of magnitude compared to classical methods without entanglement. These results show that large-scale, provable quantum advantage is achievable with current photonic technology and represent a key step toward practical quantum-enhanced learning protocols in quantum metrology and machine learning.