About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
APS March Meeting 2022
Poster
Learning via Many-Body Localized Hidden Born Machine
Abstract
Born Machines are novel generative models that leverage the probabilistic nature of the quantum states. While Born Machines based on tensor networks has shown great success learning both classical and quantum data, here, we use many-body localized states as a novel resource for learning. We present rigorous proof of expressibility of the MBL-Born Machine and show our numerical results that the driven quantum state via MBL dynamic is able to learn both MNIST data set and data from the quantum many-body state. At this end, we demonstrate that adding hidden unit boost the learnability power of the Born Machine . We further investigate the connection between disorder and the learnability power of the MBL phase by calculating various local quantities.