48 citations · 80 across the 3 of their papers we have counts for
4 papers
Boosting Method for Automated Feature Space Discovery in Supervised Quantum Machine Learning Models
Vladimir Rastunkov, Jae-Eun Park, Abhijit Mitra +5
Quantum Support Vector Machines (QSVM) have become an important tool in research and applications of quantum kernel methods. In this work we propose a boosting approach for buildin…
Practical application improvement to Quantum SVM: theory to practice
Jae-Eun Park, Brian Quanz, Steve Wood +2
Quantum machine learning (QML) has emerged as an important area for Quantum applications, although useful QML applications would require many qubits. Therefore our paper is aimed a…
A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
Arthur G. Rattew, Shaohan Hu, Marco Pistoia +2
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest. However, leading algorithms…
Computational Investigations of the Lithium Superoxide Dimer Rearrangement on Noisy Quantum Devices
Qi Gao, Hajime Nakamura, Tanvi P. Gujarati +6
Currently available noisy intermediate-scale quantum (NISQ) devices are limited by the number of qubits that can be used for quantum chemistry calculations on molecules. We show he…