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Quantum Machine Learning Quantum Foundations

Evaluating the Convergence Limit of Quantum Neural Tangent Kernel

arXiv

Authors: Trong Duong

Year

2023

Paper ID

52863

Status

Preprint

Abstract Read

~2 min

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Abstract

Quantum variational algorithms have been one of major applications of quantum computing with current quantum devices. There are recent attempts to establish the foundation for these algorithms. A possible approach is to characterize the training dynamics with quantum neural tangent kernel. In this work, we construct the kernel for two models, Quantun Ensemble and Quantum Neural Network, and show the convergence of these models in the limit of infinitely many qubits. We also show applications of the kernel limit in regression tasks.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2023 reference point for readers tracking recent quantum research.
Quantum variational algorithms have been one of major applications of quantum computing with current quantum devices.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2312.02451 [2] arXiv https://arxiv.org/abs/2312.02451 [3] Publisher https://arxiv.org/abs/2312.02451

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