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20162025
most citedDeep Reinforcement Learning-Aided Random Access

1 citations · 2 across the 6 of their papers we have counts for

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5 papers · 1 filter

quant-ph2025

Variational Quantum Integrated Sensing and Communication

Ivana Nikoloska, Osvaldo Simeone

The integration of sensing and communication functionalities within a common system is one of the main innovation drivers for next-generation networks. In this paper, we introduce…

quant-ph2025

Adaptive Bayesian Single-Shot Quantum Sensing

Ivana Nikoloska, Ruud Van Sloun, Osvaldo Simeone

Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration,…

quant-ph2025

Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inference

Ivana Nikoloska, Hamdi Joudeh, Ruud van Sloun +1

Quantum sensing exploits non-classical effects to overcome limitations of classical sensors, with applications ranging from gravitational-wave detection to nanoscale imaging. Howev…

quant-ph20221 cited

Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines

Ivana Nikoloska, Osvaldo Simeone

Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeas…

quant-ph2022

Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization

Ivana Nikoloska, Osvaldo Simeone

Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in wh…