217 citations · 803 across the 83 of their papers we have counts for
86 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…
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…
PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1
The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression. State-of-the-art works compress SViT…
PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers
Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1
Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance. However, their large sizes limit their…
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…
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…
Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks
Guilherme Cruz, Nouhaila Innan, Alberto Marchisio +2
Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep lear…
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…
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…
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…
Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models
Muhammad Shafique, Abdul Basit, Muhammad Abdullah Hanif +3
This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks w…
How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Nouhaila Innan, Antonello Rosato, Alberto Marchisio +1
Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. T…
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…
Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
Muhammad Kashif, Hanzalah Mohamed Siraj, Nouhaila Innan +2
Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends o…
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…
TRAPTI: Time-Resolved Analysis for SRAM Banking and Power Gating Optimization in Embedded Transformer Inference
Jan Klhufek, Alberto Marchisio, Vojtech Mrazek +2
Transformer neural networks achieve state-of-the-art accuracy across language and vision tasks, but their deployment on embedded hardware is hindered by stringent area, latency, an…
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…
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…
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…