output
19982026
most citedSpectral signatures of many-body localization with interacting photons

482 citations

Showing 2022Show all

11 papers · 1 filter

quant-ph2022★ 4 cited

Unravelling quantum chaos using persistent homology

Harvey Cao, Daniel Leykam, Dimitris G. Angelakis

Topological data analysis is a powerful framework for extracting useful topological information from complex datasets. Recent work has shown its application for the dynamical analy…

eess.SP2022★ 10 cited

Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis

Paris A. Karakasis, Athanasios P. Liavas, Nicholas D. Sidiropoulos +2

Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas…

eess.SP2022★ 29 cited

XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

C. Liaskos, A. Tsioliaridou, K. Georgopoulos +18

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scal…

quant-ph2022★ 4 cited

Computing Electronic Correlation Energies using Linear Depth Quantum Circuits

Chong Hian Chee, Adrian M. Mak, Daniel Leykam +2

Efficient computation of molecular energies is an exciting application of quantum computing for quantum chemistry, but current noisy intermediate-scale quantum (NISQ) devices can o…

eess.SY2022★ 60 cited

Optimal Trajectory Planning for Connected and Automated Vehicles in Lane-free Traffic with Vehicle Nudging

Venkata Karteek Yanumula, Panagiotis Typaldos, Dimitrios Troullinos +3

The paper presents a movement strategy for Connected and Automated Vehicles (CAVs) in a lane-free traffic environment with vehicle nudging by use of an optimal control approach. St…

cond-mat.mes-hall2022★ 36 cited

Topological data analysis and machine learning

Daniel Leykam, Dimitris G. Angelakis

Topological data analysis refers to approaches for systematically and reliably computing abstract ``shapes'' of complex data sets. There are various applications of topological dat…