26 citations · 44 across the 20 of their papers we have counts for
22 papers
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
Anton Firc, Nouhaila Innan, Vojtěch Staněk +6
A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining executable on real hardware. We…
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Anton Firc, Martin Perešíni, Vojtěch 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…
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…
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…
Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction
Rehan Ahmad, Muhammad Kashif, Nouhaila Innan +1
In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial task…
Investigating Different Barren Plateaus Mitigation Strategies in Variational Quantum Eigensolver
Mostafa Atallah, Nouhaila Innan, Muhammad Kashif +1
Variational Quantum Eigensolver (VQE) algorithms suffer from barren plateaus, where gradients vanish with system size and circuit depth. Although many mitigation strategies exist,…
FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm
Muhammad Kashif, Shaf Khalid, Alberto Marchisio +2
Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale…
Noisy HQNNs: A Comprehensive Analysis of Noise Robustness in Hybrid Quantum Neural Networks
Tasnim Ahmed, Alberto Marchisio, Muhammad Kashif +1
Hybrid Quantum Neural Networks (HQNNs) offer promising potential of quantum computing while retaining the flexibility of classical deep learning. However, the limitations of Noisy…
NRQNN: The Role of Observable Selection in Noise-Resilient Quantum Neural Networks
Muhammad Kashif, Muhammad Shafique
This paper explores the complexities associated with training Quantum Neural Networks (QNNs) under noisy conditions, a critical consideration for Noisy Intermediate-Scale Quantum (…
Quantum Neural Networks: A Comparative Analysis and Noise Robustness Evaluation
Tasnim Ahmed, Muhammad Kashif, Alberto Marchisio +1
In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution, combining the strengths of classical machine learning…
The Dilemma of Random Parameter Initialization and Barren Plateaus in Variational Quantum Algorithms
Muhammad Kashif, Muhammad Shafique
This paper presents an easy-to-implement approach to mitigate the challenges posed by barren plateaus (BPs) in randomly initialized parameterized quantum circuits (PQCs) within var…
Computational Advantage in Hybrid Quantum Neural Networks: Myth or Reality?
Muhammad Kashif, Alberto Marchisio, Muhammad Shafique
Hybrid Quantum Neural Networks (HQNNs) have gained attention for their potential to enhance computational performance by incorporating quantum layers into classical neural network…
Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices
Alberto Marchisio, Emman Sychiuco, Muhammad Kashif +1
The rapid advancement in Quantum Computing, particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML)…
ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks
Muhammad Kashif, Muhammad Shafique
In this paper, we present a novel framework for enhancing the performance of Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers and addressing th…
Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks
Muhammad Kashif, Emman Sychiuco, Muhammad Shafique
In this paper, we conduct a comprehensively analyze the influence of different quantum noise gates, including Phase Flip, Bit Flip, Phase Damping, Amplitude Damping, and the Depola…
HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
Muhammad Kashif, Muhammad Shafique
Effective training of Quantum Neural Networks (QNNs) is crucial in the Noisy Intermediate-Scale Quantum (NISQ) era, where noise accelerates the onset of barren plateaus (BPs) and l…
Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies
Muhammad Kashif, Muhammad Rashid, Saif Al-Kuwari +1
Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parame…