19 papers · 1 filter
Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits
Muhammad Kashif, Muhammad Shafique
Variational quantum circuits (VQCs) are typically evaluated at the logical design level when analyzing trainability. However, execution on real quantum devices requires hardware-aw…
Rethinking Expressibility-Trainability Trade-off in Hybrid Quantum Neural Networks
Muhammad Kashif, Muhammad Shafique
Hybrid quantum neural networks (HQNNs) integrate parameterized quantum circuits (PQCs) within classical networks, where the behavior of the underlying PQCs is often the primary foc…
Beyond Logical Circuits: Hardware-Aware Analysis of Expressibility and Trainability in Variational Quantum Algorithms
Muhammad Kashif, Muhammad Shafique
Variational quantum algorithms (VQAs) rely on parameterized quantum circuits (PQCs), whose performance is governed by expressibility and trainability. Existing studies typically ev…
Hybrid Quantum-Classical Neural Architecture Search
Alberto Marchisio, Muhammad Kashif, Nouhaila Innan +1
Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combi…
QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting
Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan +2
Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental setting…
Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
Jesse Roberta Mingue Njiki, Nouhaila Innan, Alberto Marchisio +3
Quantum Neural Networks (QNNs) represent a promising direction within Quantum Machine Learning (QML), yet their realization on noisy intermediate-scale quantum (NISQ) devices remai…