Exponential concentration in quantum kernel methods

• Published in: Nature communications 2024-06, Vol.15 (1), p.5200-13
• Main Authors: Thanasilp, Supanut, Wang, Samson, Cerezo, M., Holmes, Zoë
• Format: Article
• Language: English
• Subjects: 639/766/259; 639/766/483/481; Algorithms; Convexity; Data analysis; Embedding; Humanities and Social Sciences; Kernel functions; Kernels; Machine learning; multidisciplinary; Neural networks; Phase transitions; Polynomials; Quantum entanglement; Qubits (quantum computing); Random variables; Science; Science & Technology - Other Topics; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-49287-w

Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guaranteed to find the optimal model’s parameters due to the convexity of the training landscape. However, this is based on the assumption that the quantum kernel can be efficiently obtained from quantum hardware. In this work we study the performance of quantum kernel models from the perspective of the resources needed to accurately estimate kernel values. We show that, under certain conditions, values of quantum kernels over different input data can be exponentially concentrated (in the number of qubits) towards some fixed value. Thus on training with a polynomial number of measurements, one ends up with a trivial model where the predictions on unseen inputs are independent of the input data. We identify four sources that can lead to concentration including expressivity of data embedding, global measurements, entanglement and noise. For each source, an associated concentration bound of quantum kernels is analytically derived. Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration. Our results are verified through numerical simulations for several QML tasks. Altogether, we provide guidelines indicating that certain features should be avoided to ensure the efficient evaluation of quantum kernels and so the performance of quantum kernel methods. Quantum kernel methods are usually believed to enjoy better trainability than quantum neural networks which may suffer from a well-studied barren plateau. Here, building over previous evidence, the authors show that practical implications of exponential concentration result in a trivial data-insensitive model after training, and identify commonly used features that induce the concentration.