On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features

• URL: http://arxiv.org/abs/2505.15902v2
• Date: Mon, 26 May 2025 16:24:23 GMT
• Title: On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features
• Authors: Mehrad Sahebi, Alice Barthe, Yudai Suzuki, Zoë Holmes, Michele Grossi,
• Abstract summary: We derive bounds on the generalization performance gap between classical RFF models and quantum models for regression and classification tasks.<n>Our findings not only broaden the applicability of RFF-based dequantization but also enhance the understanding of potential quantum advantages in practical machine-learning tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the quest for quantum advantage, a central question is under what conditions can classical algorithms achieve a performance comparable to quantum algorithms--a concept known as dequantization. Random Fourier features (RFFs) have demonstrated potential for dequantizing certain quantum neural networks (QNNs) applied to regression tasks, but their applicability to other learning problems and architectures remains unexplored. In this work, we derive bounds on the generalization performance gap between classical RFF models and quantum models for regression and classification tasks with both QNN and quantum kernel architectures. We support our findings with numerical experiments that illustrate the practical dequantization of existing quantum kernel-based methods. Our findings not only broaden the applicability of RFF-based dequantization but also enhance the understanding of potential quantum advantages in practical machine-learning tasks.

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