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 channel. By harnessing the unique properties of continuous-variable quantum entanglement, we achieve high-precision reconstruction of the displacement distribution using multiple orders of magnitude fewer experiments compared to methods that do not employ entangled resources. Specifically, with approximately $5$ dB of two-mode squeezing---corresponding to imperfect Einstein--Podolsky--Rosen (EPR) entanglement---we successfully reconstruct a 100-mode bosonic displacement channel, requiring $10^{11}$ fewer experiments than a conventional measurement 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.
