3 citations · 4 across the 4 of their papers we have counts for
4 papers
Non-variational supervised quantum kernel methods: a review
John Tanner, Chon-Fai Kam, Jingbo Wang
Quantum kernel methods (QKMs) have emerged as a prominent framework for supervised quantum machine learning. Unlike variational quantum algorithms, which rely on gradient-based opt…
Maritime object classification with SAR imagery using quantum kernel methods
John Tanner, Nicholas Davies, Pascal Jahan Elahi +5
Illegal, unreported, and unregulated (IUU) fishing causes global economic losses of 10-25 billion USD annually and undermines marine sustainability and governance. Synthetic Apertu…
Learning out-of-time-ordered correlators with classical kernel methods
John Tanner, Jason Pye, Jingbo Wang
Out-of-Time Ordered Correlators (OTOCs) are widely used to investigate information scrambling in quantum systems. However, directly computing OTOCs with classical computers is an e…
Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine
Shengxin Zhuang, John Tanner, Yusen Wu +7
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a…