Potential and limitations of random Fourier features for dequantizing quantum machine learning

• DOI: 10.48550/arxiv.2309.11647
• Publication Date: 2023-01-01
• Authors: Ryan Sweke; Erik Recio; Sofiène Jerbi; Elies Gil-Fuster; Bryce Fuller; Jens Eisert; Johannes Jakob Meyer
• Keywords: Quantum Machine Learning

Quantum machine learning is arguably one of the most explored applications near-term quantum devices. Much focus has been put on notions variational where parameterized circuits (PQCs) are used as models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via random Fourier features (RFF). In this work, we establish necessary and sufficient conditions under RFF does indeed provide an for regression. We build these insights make concrete suggestions architecture design, identify structures regression problem admit potential advantage based optimization.