Quantum learning advantage on a scalable photonic platform
Abstract
Recent advancements in quantum technologies have opened new horizons for exploring the physical world in ways once deemed impossible. Central to these breakthroughs is the concept of quantum advantage, where quantum systems outperform their classical counterparts in solving specific tasks. While much attention has been devoted to computational speedups, quantum advantage in learning physical systems remains a largely untapped frontier. Here, we present a photonic implementation of a quantum-enhanced protocol for learning the probability distribution of a multimode bosonic displacement process. By harnessing the unique properties of continuous-variable quantum entanglement, we obtain a massive advantage in sample complexity with respect to conventional methods without entangled resources. With approximately 5 dB of two-mode squeezing -- corresponding to imperfect Einstein--Podolsky--Rosen (EPR) entanglement -- we learn a 100-mode bosonic displacement process using 11.8 orders of magnitude fewer samples than a conventional scheme. Our results demonstrate that even with non-ideal, noisy entanglement, a significant quantum advantage can be realized in continuous-variable quantum systems. This marks an important step towards practical quantum-enhanced learning protocols with implications for quantum metrology, certification, and machine learning.
Cite
@article{arxiv.2502.07770,
title = {Quantum learning advantage on a scalable photonic platform},
author = {Zheng-Hao Liu and Romain Brunel and Emil E. B. Østergaard and Oscar Cordero and Senrui Chen and Yat Wong and Jens A. H. Nielsen and Axel B. Bregnsbo and Sisi Zhou and Hsin-Yuan Huang and Changhun Oh and Liang Jiang and John Preskill and Jonas S. Neergaard-Nielsen and Ulrik L. Andersen},
journal= {arXiv preprint arXiv:2502.07770},
year = {2025}
}
Comments
8+23 pages, 3+10 figures