What can we learn from quantum convolutional neural networks? C Umeano, AE Paine, VE Elfving, O Kyriienko
Advanced Quantum Technologies, 2400325, 2023
10 2023 What can we learn from quantum convolutional neural networks?(2023) C Umeano, AE Paine, VE Elfving, O Kyriienko
arXiv preprint arXiv:2308.16664, 0
6 Quantum topological data analysis via the estimation of the density of states S Scali, C Umeano, O Kyriienko
Physical Review A 110 (4), 042616, 2024
4 2024 Ground state-based quantum feature maps C Umeano, O Kyriienko
arXiv preprint arXiv:2404.07174, 2024
4 2024 The topology of data hides in quantum thermal states S Scali, C Umeano, O Kyriienko
APL Quantum 1 (3), 2024
2 2024 Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids C Umeano, F Jamet, LP Lindoy, I Rungger, O Kyriienko
arXiv preprint arXiv:2407.04205, 2024
1 2024 Geometric quantum machine learning of BQP C Umeano, VE Elfving, O Kyriienko
arXiv preprint arXiv:2402.03871, 2024
1 2024 Quantum Algorithms: A Review C Umeano
Imperial College London, 2021
1 2021 Quantum community detection via deterministic elimination C Umeano, S Scali, O Kyriienko
arXiv preprint arXiv:2412.13160, 2024
2024 Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification? C Umeano, S Scali, O Kyriienko
arXiv preprint arXiv:2409.01496, 2024
2024 Quantum topological data analysis: using Fourier analysis to learn topological properties S Scali, O Kyriienko, C Umeano
APS March Meeting Abstracts 2024, B51. 003, 2024
2024 
Oleksandr KyriienkoProfessor at the University of SheffieldEmail verificata su sheffield.ac.uk