Quantum learning advantage on a scalable photonic platform

TL;DR

This paper demonstrates a photonic quantum protocol that significantly reduces the number of samples needed to learn complex physical processes, showcasing practical quantum advantage in continuous-variable systems.

Contribution

It introduces a scalable photonic implementation of a quantum-enhanced learning protocol that achieves substantial sample complexity reduction using imperfect entanglement.

Findings

Achieved ~11.8 orders of magnitude reduction in samples needed.

Demonstrated quantum advantage with 5 dB of two-mode squeezing.

Applicable to learning 100-mode bosonic displacement processes.

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.