29 citations · 43 across the 5 of their papers we have counts for
5 papers
Quantum feature encoding optimization
Tommaso Fioravanti, Brian Quanz, Gabriele Agliardi +3
Quantum Machine Learning (QML) holds the promise of enhancing machine learning modeling in terms of both complexity and accuracy. A key challenge in this domain is the encoding of…
Quantum Multiple Kernel Learning in Financial Classification Tasks
Shungo Miyabe, Brian Quanz, Noriaki Shimada +11
Financial services is a prospect industry where unlocked near-term quantum utility could yield profitable potential, and, in particular, quantum machine learning algorithms could p…
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…
Machine learning based co-creative design framework
Brian Quanz, Wei Sun, Ajay Deshpande +2
We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs. We demo…