681 citations · 844 across the 18 of their papers we have counts for
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Paving the Way to Hybrid Quantum-Classical Scientific Workflows
Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic +1
The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scien…
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It…
On Optimizing Hyperparameters for Quantum Neural Networks
Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usu…
Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing
Vincenzo De Maio, Meerzhan Kanatbekova, Felix Zilk +3
As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum…
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which…