activity
20042026
most citedExtending Hudson's theorem to mixed quantum states

55 citations · 227 across the 30 of their papers we have counts for

collaborators
Showing quant-phShow all

31 papers · 1 filter

quant-ph2026

Optimal Entanglement Routing in Quantum Repeater Chains: Beyond Fixed Operation Order and Purification Schedule

Aikaterini Mandilara, Antonia Tsili, Dimitris Syvridis +1

Entanglement routing establishes entangled pairs between distant nodes of a quantum network by purifying and swapping pairs generated on elementary links. Existing methods typicall…

quant-ph2026

Let the Qudit Do the Jacobi: A Structured Quantum Algorithm for Spectral Decomposition

A. Mandilara

Jacobi diagonalization is a long-established numerical algorithm for the spectral decomposition of Hermitian and, more generally, normal matrices. In this work, we develop a qudit-…

quant-ph2026

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos +1

We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum ci…

quant-ph2026

Quantum-Inspired Unitary Pooling for Multispectral Satellite Image Classification

Georgios Maragkopoulos, Aikaterini Mandilara, Ralntion Komini +1

Multispectral satellite imagery poses significant challenges for deep learning models due to the high dimensionality of spectral data and the presence of structured correlations ac…

quant-ph2025

Feature Ranking in Credit-Risk with Qudit-Based Networks

Georgios Maragkopoulos, Lazaros Chavatzoglou, Aikaterini Mandilara +1

In finance, predictive models must balance accuracy and interpretability, particularly in credit risk assessment, where model decisions carry material consequences. We present a qu…

quant-ph2025

Learning kernels with quantum optical circuits

A. Mandilara, A. D. Papadopoulos, D. Syvridis

Support Vector Machines (SVMs) are a cornerstone of supervised learning, widely used for data classification. A central component of their success lies in kernel functions, which e…