15 citations · 34 across the 9 of their papers we have counts for
9 papers
Advantage for Discrete Variational Quantum Algorithms in Circuit Recompilation
Oleksandr Kyriienko, Chukwudubem Umeano, Zoë Holmes
The relative power of quantum algorithms, using an adaptive access to quantum devices, versus classical post-processing methods that rely only on an initial quantum data set, remai…
Quantum community detection via deterministic elimination
Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko
We propose a quantum algorithm for calculating the structural properties of complex networks and graphs. The corresponding protocol -- deteQt -- is designed to perform large-scale…
Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
Chukwudubem Umeano, Stefano Scali, Oleksandr Kyriienko
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum m…
Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids
Chukwudubem Umeano, François Jamet, Lachlan P. Lindoy +2
We develop a quantum simulation-based approach for studying properties of strongly correlated magnetic materials at increasing scale. We consider a paradigmatic example of a quantu…
Ground state-based quantum feature maps
Chukwudubem Umeano, Oleksandr Kyriienko
We introduce a quantum data embedding protocol based on the preparation of a ground state of a parameterized Hamiltonian. We analyze the corresponding quantum feature map, recastin…
Geometric quantum machine learning of BQP protocols and latent graph classifiers
Chukwudubem Umeano, Vincent E. Elfving, Oleksandr Kyriienko
Geometric quantum machine learning (GQML) aims to embed problem symmetries for learning efficient solving protocols. However, the question remains if (G)QML can be routinely used f…
The topology of data hides in quantum thermal states
Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko
We provide a quantum protocol to perform topological data analysis (TDA) via the distillation of quantum thermal states. Recent developments of quantum thermal state preparation al…
Quantum topological data analysis via the estimation of the density of states
Stefano Scali, Chukwudubem Umeano, Oleksandr Kyriienko
We develop a quantum topological data analysis (QTDA) protocol based on the estimation of the density of states (DOS) of the combinatorial Laplacian. Computing topological features…
What can we learn from quantum convolutional neural networks?
Chukwudubem Umeano, Annie E. Paine, Vincent E. Elfving +1
Quantum machine learning (QML) shows promise for analyzing quantum data. A notable example is the use of quantum convolutional neural networks (QCNNs), implemented as specific type…