217 citations · 803 across the 83 of their papers we have counts for
37 papers · 1 filter
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…
Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Prediction
Alberto Marchisio, Hanzalah Mohamed Siraj, Muhammad Kashif +2
Photonic quantum computing has recently emerged as a promising platform for hybrid quantum machine learning due to its native realization of linear-optical circuits and the computa…
PN-QNN: Harnessing Physical Noise as a Native Regularizer in Photonic Hybrid Quantum Neural Networks
Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2
Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress. We ask whether it can instead act as a hardware-native regularizer for photonic hybrid qu…
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…
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…
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…
EFaaS: A Quantum-Classical Serverless Entangled Scheduler for Hybrid Variational Algorithms
Abolfazl Younesi, Nouhaila Innan, Alberto Marchisio +1
As quantum computing enters the Utility Era, realizing near-term advantage relies heavily on Hybrid Variational Quantum Algorithms (VQAs). These algorithms require a tightly couple…
Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2
Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimi…
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…
GAT-QNN: Genetic Algorithm-Based Training of Hybrid Quantum Neural Networks
Tasnim Ahmed, Alberto Marchisio, Muhammad Kashif +2
Hybrid Quantum Neural Networks (HQNNs) combine classical learning with parameterized quantum circuits, but their practical performance is often limited by (i) the noise of Noisy In…
QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
Kooshan Maleki, Alberto Marchisio, Muhammad Shafique
Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expressivity, trainability, and res…
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…
Evaluating Quantum Amplitude Estimation for Pricing Multi-Asset Basket Options
Muhammad Kashif, Shaf Khalid, Nouhaila Innan +2
Accurate and efficient pricing of multi-asset basket options poses a significant challenge, especially when dealing with complex real-world data. In this work, we investigate the r…
RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio +3
Quantum Federated Learning (QFL) merges privacy-preserving federation with quantum computing gains, yet its resilience to adversarial noise is unknown. We first show that QFL is as…
QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance
Nouhaila Innan, Muhammad Kashif, Alberto Marchisio +3
The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the…
Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy
Nouhaila Innan, Muhammad Kashif, Alberto Marchisio +2
This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks (QNNs) in the Noisy Intermediate-Scale Quantum…
Quantum Portfolio Optimization with Expert Analysis Evaluation
Nouhaila Innan, Ayesha Saleem, Alberto Marchisio +1
Quantum algorithms have gained increasing attention for addressing complex combinatorial problems in finance, notably portfolio optimization. This study systematically benchmarks t…
PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on re…
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…
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…
Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
Owais Ishtiaq Siddiqui, Nouhaila Innan, Alberto Marchisio +2
Quantum Machine Learning (QML) has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling. However, its practical im…
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)…