Adaptive Quantum Generative Training using an Unbounded Loss Function

• URL: http://arxiv.org/abs/2408.00218v1
• Date: Thu, 1 Aug 2024 01:04:53 GMT
• Title: Adaptive Quantum Generative Training using an Unbounded Loss Function
• Authors: Kyle Sherbert, Jim Furches, Karunya Shirali, Sophia E. Economou, Carlos Ortiz Marrero,
• Abstract summary: We propose a generative quantum learning algorithm, R'enyi-ADAPT, using the Adaptive Derivative-Assembled Problem Tailored ansatz framework. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We show that R'enyi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal R'enyi divergence loss function.
• Score: 1.0485739694839669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a generative quantum learning algorithm, R\'enyi-ADAPT, using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum R\'enyi 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 R\'enyi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal R\'enyi divergence loss function.

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