109 citations · 188 across the 10 of their papers we have counts for
10 papers
Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge +4
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks.…
Evaluation of derivatives using approximate generalized parameter shift rule
Vytautas Abramavicius, Evan Philip, Kaonan Micadei +5
Parameter shift rules are instrumental for derivatives estimation in a wide range of quantum algorithms, especially in the context of Quantum Machine Learning. Application of singl…
Application of quantum-inspired generative models to small molecular datasets
C. Moussa, H. Wang, M. Araya-Polo +2
Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-t…
Resource frugal optimizer for quantum machine learning
Charles Moussa, Max Hunter Gordon, Michal Baczyk +3
Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms hav…
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
Charles Moussa, Jan N. van Rijn, Thomas Bäck +1
As restricted quantum computers are slowly becoming a reality, the search for meaningful first applications intensifies. In this domain, one of the more investigated approaches is…
Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm
Charles Moussa, Hao Wang, Thomas Bäck +1
As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of para…