IQUIST Special Seminar: Sabre Kais, Purdue University

In this talk, I will focus on quantum machine learning, particularly the Restricted Boltzmann Machine (RBM), as it  emerged to be a promising alternative approach  leveraging  the power of quantum computers. Such algorithms have been developed to solve problems like electronic structure calculations of molecular systems and spin models in magnetic systems. However, the discussion in all these recipes focuses specifically on targeting the ground state. Herein we demonstrate a quantum algorithm that can filter any energy eigenstate of the system based on either symmetry properties or a predefined choice of the user. The workhorse of our technique is a shallow neural network encoding the desired state of the system with the amplitude computed by sampling the Gibbs−Boltzmann distribution using a quantum circuit and the phase information obtained classically from the nonlinear activation of a separate set of neurons. We implement our algorithm not only on quantum simulators but also on actual IBM-Q quantum devices and show good agreement with the results procured from conventional electronic structure calculations.

Finally,  I will   discuss and illustrate that the imaginary components of out-of-time correlators can be related to conventional measures of correlation like mutual information. Such an analysis offers important insights into the training dynamics by unraveling how quantum information is scrambled through such a network introducing correlation among its constituent sub-systems. This approach not only demystifies the training of quantum machine learning models but can also explicate the capacitive quality of the model.

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