Born machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states. Here, we present a new architecture called many-body localized (MBL) hidden Born machine that utilizes both MBL dynamics and hidden units as learning resources. We show that the hidden units act as an effective thermal bath that enhances the trainability of the system, while the MBL dynamics stabilize the training trajectories. We numerically demonstrate that the MBL hidden Born machine is capable of learning a variety of tasks, including a toy version of MNIST handwritten digits, quantum data obtained from quantum many-body states, and non-local parity data. Our architecture and algorithm provide novel strategies of utilizing quantum many-body systems as learning resources, and reveal a powerful connection between disorder, interaction, and learning in quantum many-body systems.

翻译：玻恩机是一种受量子启发的生成模型，利用了量子态的概率特性。本文提出一种名为多体局域化（MBL）隐式玻恩机的新架构，同时利用MBL动力学和隐单元作为学习资源。我们证明隐单元充当有效热浴，增强了系统的可训练性，而MBL动力学则稳定了训练轨迹。数值实验表明，MBL隐式玻恩机能够学习多种任务，包括MNIST手写数字的简化版本、从量子多体态获得的量子数据以及非局域奇偶校验数据。我们的架构和算法为利用量子多体系统作为学习资源提供了新策略，并揭示了量子多体系统中无序、相互作用与学习之间的深刻联系。