8 papers
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…
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extendi…
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly…
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta +4
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with q…