Adaptive Quantum Generative Training using an Unbounded Loss Function

We propose a generative quantum learning algorithm using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum Rényi divergence of order two, an unbounded function that mitigates barren plateaus which inhibit training variational circuits. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We perform numerical experiments of up to 12 qubits comparing our method learning algorithms that use linear objective functions and show that Rényi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal Rényi divergence loss function.. Authors: Sherbert, Kyle [Virginia Tech]; Furches, James C. [BATTELLE (PACIFIC NW LAB)]; Shirali, Karunya [Virginia Tech]; Economou, Sophia [Virginia Tech]; Ortiz Marrero, Carlos M. [BATTELLE (PACIFIC NW LAB)]. DOE Contract: AC05-76RL01830. Subjects: quantum algorithms, quantum machine learning, quantum computing