论文标题
多体局部隐藏生成模型
Many-body localized hidden generative models
论文作者
论文摘要
出生的机器是量子启发的生成模型，可利用量子状态的概率性质。在这里，我们提出了一种称为多体局部（MBL）隐藏机器的新体系结构，它利用MBL动力学和隐藏单元作为学习资源。我们表明，隐藏单元充当有效的热浴，可增强系统的训练性，而MBL动力学稳定训练轨迹。我们从数值上证明，MBL隐藏的机器能够学习各种任务，包括MNIST手写数字的玩具版本，从量子多体型状态获得的量子数据以及非本地奇偶校验数据。我们的体系结构和算法提供了利用量子多体系统作为学习资源的新型策略，并在量子多体系统中揭示了障碍，互动和学习之间的强大联系。
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.
