2025-09-25 デンマーク工科大学（DTU）
The squeezer – an optical parametric oscillator (OPO) that uses a nonlinear crystal inside an optical cavity to manipulate the quantum fluctuations of light. – is responsible for the entanglement. Photo: Jonas Schou Neergaard-Nielsen.
＜関連情報＞
- https://physics.dtu.dk/about/news/proven-quantum-advantage-researchers-cut-the-time-for-a-learning-task-from-20-million-years-to-15-mi
- https://www.science.org/doi/10.1126/science.adv2560
- https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.133.230604
スケーラブルな光子プラットフォームにおける量子学習の優位性 Quantum learning advantage on a scalable photonic platform
Zheng-Hao Liu, Romain Brunel, Emil E. B. Østergaard, Oscar Cordero, […] , and Ulrik L. Andersen
Science Published:25 Sep 2025
DOI:https://doi.org/10.1126/science.adv2560
Editor’s summary
Understanding the properties of a system requires making measurements of a variable and then using that information to piece together a detailed understanding. As the system becomes more complex, the number of required measurements increases exponentially, placing fundamental limits on obtaining that information using classical means. Liu et al. demonstrated a quantum learning advantage in an optical system using entangled photons as probes to reduce the sampling complexity of the process by more than 11 orders of magnitude compared with classical probes. This result highlights a route to quantum enhanced learning protocols for machine learning and developing quantum technologies. — Ian S. Osborne
Abstract
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.
ボソンランダム変位チャネルの学習におけるエンタングルメント対応の利点 Entanglement-Enabled Advantage for Learning a Bosonic Random Displacement Channel
Changhun Oh, Senrui Chen, Yat Wong, Sisi Zhou, Hsin-Yuan Huang, Jens A. H. Nielsen, Zheng-Hao Liu, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen et al.
Physical Review Letters Published: 6 December, 2024
DOI: https://doi.org/10.1103/PhysRevLett.133.230604
Abstract
We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a probabilistic mixture of displacement operators acting on bosonic modes, called a random displacement channel. We prove that if the modes are not entangled with an ancillary quantum memory, then the channel must be sampled a number of times exponential in in order to estimate its characteristic function to reasonable precision; this lower bound on sample complexity applies even if the channel inputs and measurements performed on channel outputs are chosen adaptively or have unrestricted energy. On the other hand, we present a simple entanglement-assisted scheme that only requires a number of samples independent of in the large squeezing and noiseless limit. This establishes an exponential separation in sample complexity. We then analyze the effect of photon loss and show that the entanglement-assisted scheme is still significantly more efficient than any lossless entanglement-free scheme under mild experimental conditions. Our work illuminates the role of entanglement in learning CV systems and points toward experimentally feasible demonstrations of provable entanglement-enabled advantage using CV quantum platforms.
