Quantum learning advantage on a scalable photonic platform
Zhenghao Liu, Romain Brunel, Erling Østergaard, Oscar Cordero, Senrui Chen, Yat Wong, Jens Arnbak Holbøll Nielsen, Axel B. Bregnsbo, Sisi Zhou, Hsin-Yuan Huang, Changhun Oh, Liang Jiang, John P. Preskill, Jonas Schou Neergaard-Nielsen, Ulrik L. Andersen · Science · 2025
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.