量子纠缠实现可证明的鲁棒学习优势，突破经典模型极限。
Entanglement-induced provable and robust quantum learning advantages
- 利用量子纠缠减少非局域任务通信开销，实现指数级性能提升。
- 量子模型仅需常数参数即可完美解决任务，经典模型需线性增长才能达高精度。
- 实验验证在离子阱设备上具备抗噪能力，适合当前量子硬件部署。
量子计算在提升机器学习方面具有巨大潜力，但迄今尚未实现量子学习优势的明确证明。本文通过信息论方法严格证明，在表达能力、推理速度和训练效率上，量子模型相比常用经典模型具备无条件且抗噪声的量子学习优势。其核心机制在于：量子纠缠能减少非局域任务所需的通信量。我们设计了一项任务，量子模型仅用常数规模参数即可确定性求解，而经典模型需线性扩展才能达到大于指数小的准确率。量子模型训练资源恒定，且对常数级别噪声保持鲁棒。通过数值模拟与IonQ Aria上的囚禁离子实验，成功验证了该优势。结果为在当前含噪声中等规模量子设备上实现量子学习优势提供了关键指导。
原文摘要 · Abstract (English)
Quantum computing holds unparalleled potentials to enhance machine learning. However, a demonstration of quantum learning advantage has not been achieved so far. We make a step forward by rigorously establishing a noise-robust, unconditional quantum learning advantage in expressivity, inference speed, and training efficiency, compared to commonly-used classical models. Our proof is information-theoretic and pinpoints the origin of this advantage: entanglement can be used to reduce the communication required by non-local tasks. In particular, we design a task that can be solved with certainty by quantum models with a constant number of parameters using entanglement, whereas commonly-used classical models must scale linearly to achieve a larger-than-exponentially-small accuracy. We show that the quantum model is trainable with constant resources and robust against constant noise. Through numerical and trapped-ion experiments on IonQ Aria, we demonstrate the desired advantage. Our results provide valuable guidance for demonstrating quantum learning advantages with current noisy intermediate-scale devices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。