17 citations · 45 across the 29 of their papers we have counts for
31 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
Recent advances in Large Language Models (LLMs) have demonstrated strong potential in code generation, yet their effectiveness in quantum computing remains underexplored. This pape…
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…
A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
Abdul Basit, Nouhaila Innan, Muhammad Haider Asif +4
Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software de…
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…