35 citations · 64 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Investigate the Performance of Distribution Loading with Conditional Quantum Generative Adversarial Network Algorithm on Quantum Hardware with Error Suppression
Anh Pham, Andrew Vlasic
The study examines the efficacy of the Fire Opal error suppression and AI circuit optimization system integrated with IBM's quantum computing platform for a multi-modal distributio…
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
Michael Vitz, Hamed Mohammadbagherpoor, Samarth Sandeep +3
To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learnin…