8 citations · 20 across the 5 of their papers we have counts for
5 papers
Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
Ying Chen, Paul Griffin, Paolo Recchia +2
Recovery rate prediction plays a pivotal role in bond investment strategies by enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supportin…
QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld +2
Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of…
Quantum Machine Learning for Credit Scoring
Nikolaos Schetakis, Davit Aghamalyan, Michael Boguslavsky +3
In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium-sized enterprises (SME). A quantum/classical hybrid approach has b…
Exponential Qubit Reduction in Optimization for Financial Transaction Settlement
Elias X. Huber, Benjamin Y. L. Tan, Paul R. Griffin +1
We extend the qubit-efficient encoding presented in [Tan et al., Quantum 5, 454 (2021)] and apply it to instances of the financial transaction settlement problem constructed from d…
Binary classifiers for noisy datasets: a comparative study of existing quantum machine learning frameworks and some new approaches
N. Schetakis, D. Aghamalyan, M. Boguslavsky +1
One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Com…