Quantum learning advantage on a scalable photonic platform
- Zheng-Hao Liu
- Romain Brunel
- Emil E. B. Østergaard
- Oscar Cordero
- Senrui Chen
- Yat Wong
- Jens A. H. Nielsen
- Axel B. Bregnsbo
- Sisi Zhou
- Hsin-Yuan Huang
- Changhun Oh
- Liang Jiang
- John Preskill
- Jonas S. Neergaard-Nielsen
- Ulrik L. Andersen
2025-09-25
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.