5 papers · 1 filter
Quantum Gaussian processes for prediction of channel observations
Jonas Jäger, Yaroslav Khmelnitskiy, Paolo Braccia +4
Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited numb…
Large-Scale Quantum Kernels for Hyperspectral Data Classification
A. Delilbasic, A. Miroszewski, A. Wijata +4
Quantum kernel methods have emerged as a promising approach for leveraging high-dimensional feature spaces in machine learning, particularly in domains where classical kernel metho…
Light-cone feature selection in methane hyperspectral images
Artur Miroszewski, Jakub Nalepa, Agata M. Wijata
Hyperspectral images (HSIs) capture detailed spectral information across numerous contiguous bands, enabling the extraction of intrinsic characteristics of scanned objects and area…
To center or not to center? Hyperspectral data vs. quantum covariance matrices
Artur Miroszewski, Jakub Nalepa, Agata M. Wijata
We highlight how the normalization required for embedding data in quantum states affects data centering, which can significantly influence quantum amplitude-encoded covarianc…
In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods
Artur Miroszewski, Marco Fellous Asiani, Jakub Mielczarek +2
Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum natu…