27 papers
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…
PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
Minghao Shao, Nouhaila Innan, Hariharan Janardhanan +3
The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistants: general-purpose models hall…
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…
Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation
Mohamed Khair Altrabulsi, Nouhaila Innan, Alberto Marchisio +2
Adaptive robot navigation in dynamic environments requires policies that can reach the target reliably while producing efficient and stable trajectories. This paper presents Q-SpiR…
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…