7 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…
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…
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…
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…
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…