3 citations · 3 across the 6 of their papers we have counts for
6 papers
Solving 2D Black Scholes Equation via Hermitian Block Embedding and Generalised Quantum Signal Processing
James W. Greenwell, Jingbo Wang, Des Hill
The Black Scholes equation provides a fundamental model for the no arbitrage pricing of financial derivatives. After finite difference discretisation, the pricing problem can be fo…
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…
Efficient Noisy Quantum State and Process Tomography
Chenyang Li, Shengxin Zhuang, Yukun Zhang +4
Efficiently characterizing large quantum states and processes is a central yet notoriously challenging task in quantum information science, as conventional tomography methods typic…
Monte-Carlo Option Pricing in Quantum Parallel
Robert Scriba, Yuying Li, Jingbo B Wang
Financial derivative pricing is a significant challenge in finance, involving the valuation of instruments like options based on underlying assets. While some cases have simple sol…
Quantum Computing for Option Portfolio Analysis
Yusen Wu, Jingbo B. Wang, Yuying Li
In this paper, we introduce an efficient and end-to-end quantum algorithm tailored for computing the Value-at-Risk (VaR) and conditional Value-at-Risk (CVar) for a portfolio of Eur…
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…