Quantum fidelity kernel with a trapped-ion simulation platform

• URL: http://arxiv.org/abs/2311.18719v2
• Date: Mon, 15 Apr 2024 08:02:46 GMT
• Title: Quantum fidelity kernel with a trapped-ion simulation platform
• Authors: Rodrigo Martínez-Peña, Miguel C. Soriano, Roberta Zambrini,
• Abstract summary: Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. We propose trapped-ion simulation platforms as a means to compute quantum kernels and demonstrate their effectiveness for binary classification tasks. The results show that ion trap platforms are well-suited for quantum kernel computation and can achieve high accuracy with only a few qubits.
• Score: 3.9940425551415597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels becomes an issue. To overcome these challenges, various strategies under the concept of inductive bias have been proposed. Bandwidth optimization is a promising approach that can be implemented using quantum simulation platforms. We propose trapped-ion simulation platforms as a means to compute quantum kernels and demonstrate their effectiveness for binary classification tasks. We compare the performance of the proposed method with an optimized classical kernel and evaluate the robustness of the quantum kernel against noise. The results show that ion trap platforms are well-suited for quantum kernel computation and can achieve high accuracy with only a few qubits.

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