19 papers
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Anton Firc, Martin PereÅ¡Ãni, VojtÄch Mrázek +7
Hybrid quantum-classical machine learning workflows repeatedly evaluate many small parametrized circuits during training and model exploration. In this regime, framework dispatch a…
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…
Q-RAIL: A Reliability-Aware Framework for Quantum Federated Learning on Heterogeneous Noisy Hardware
Walid El Maouaki, Muhammad Shafique
Quantum federated learning (QFL) on NISQ hardware is highly sensitive to backend heterogeneity: some clients contribute informative updates, while others contribute noise-dominated…
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…
QGCL: Quantum-Guided Clause Learning for Cryptanalytic SAT
Walid El Maouaki, Alberto Marchisio, Muhammad Shafique
Power side-channel attacks on AES exploit data-dependent physical leakage to recover secret keys, but turning noisy leakage observations into a verified AES-128 key remains a hard…
Q-LEAK: Quantum-Based LEAKage Verification for Side-Channel Countermeasures
Walid El Maouaki, Alberto Marchisio, Muhammad Shafique
Formal verification of power side-channel leakage and its countermeasures in cryptographic algorithms is challenging, as SAT-based methods fail to scale on XOR-heavy, time-unrolled…