  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.
