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Nicholas S. DiBrita

2 accepted papers

2026

Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus

ICLR 2026poster

Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for distributed QML in a quantum federated learning (QFL) format. However, the quantum computers that different clients have acc…

Cited by 0SourcecodeScholar
2025

ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers

ICCV 2025poster

Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which ai…