Training embedding quantum kernels with data re-uploading quantum neural networks

P Rodriguez-Grasa, Y Ban, M Sanz - arXiv preprint arXiv:2401.04642, 2024 - arxiv.org
Kernel methods play a crucial role in machine learning and the Embedding Quantum
Kernels (EQKs), an extension to quantum systems, have shown very promising …

Quantum fidelity kernel with a trapped-ion simulation platform

R Martínez-Peña, MC Soriano, R Zambrini - Physical Review A, 2024 - APS
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 …

Benchmarking of quantum fidelity kernels for Gaussian process regression

X Guo, J Dai, RV Krems - Machine Learning: Science and …, 2024 - iopscience.iop.org
Quantum computing algorithms have been shown to produce performant quantum kernels
for machine-learning classification problems. Here, we examine the performance of …

The power of one clean qubit in supervised machine learning

M Karimi, A Javadi-Abhari, C Simon, R Ghobadi - Scientific Reports, 2023 - nature.com
This paper explores the potential benefits of quantum coherence and quantum discord in the
non-universal quantum computing model called deterministic quantum computing with one …

Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning

J Mancilla, A Sequeira, T Tagliani, F Llaneza… - arXiv preprint arXiv …, 2024 - arxiv.org
Quantum Kernels are projected to provide early-stage usefulness for quantum machine
learning. However, highly sophisticated classical models are hard to surpass without losing …

Quantum Kernel Based Data Fusion

L Kunczik, S Tornow - 2022 25th International Conference on …, 2022 - ieeexplore.ieee.org
For quantum classification methods, the quantum kernel has a role in encoding the data set
from its original low-dimensional real space into a high-dimensional quantum state space …