Work

Adaptive Quantum Generative Training using an Unbounded Loss Function

Year: 2024

Type: conference-paper

Abstract: We propose a generative quantum learning algorithm, Rényi-ADAPT, 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 on systems of up to 12 qubits, comparing our method to 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 Rénvi divergence loss function.

Source: IEEE International Conference on Quantum Computing and Engineering (QCE)

DOI: 10.1109/qce60285.2024.00202

Authors: Kyle Sherbert, Jim Furches, Karunya Shirali, Sophia E. Economou, Carlos Ortiz Marrero

Institutions: Virginia Tech, Pacific Northwest National Laboratory

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science

Awards: G241279073, G357145406, G5765906131, G7463374569