5 papers
Quantum Circuits, Feature Maps, and Expanded Pseudo-Entropy: Analysis of Encoding Real-World Data into a Quantum Computer
Andrew Vlasic, Payal Solanki, Anh Pham
This manuscript introduces a computationally efficient method to calculate the nonlinearity of a quantum feature map, as well as a method for determining whether a quantum feature…
Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines
Payal D. Solanki, Anh Pham
Quantum Extreme Learning Machine (QELM) is an emerging hybrid quantum machine learning framework that leverages quantum system dynamics to enhance classical models. However, QELM c…
Empirical Power of Quantum Encoding Methods for Binary Classification
Gennaro De Luca, Andrew Vlasic, Michael Vitz +1
Quantum machine learning is one of the many potential applications of quantum computing, each of which is hoped to provide some novel computational advantage. However, quantum mach…
Towards structure-preserving quantum encodings
Arthur J. Parzygnat, Tai-Danae Bradley, Andrew Vlasic +1
Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are c…
Robust Quantum Reservoir Computing for Molecular Property Prediction
Daniel Beaulieu, Milan Kornjaca, Zoran Krunic +4
Machine learning has been increasingly utilized in the field of biomedical research to accelerate the drug discovery process. In recent years, the emergence of quantum computing ha…