Profil bibliographique

Muazzam Ali

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

14Publications signalées

0Citations signalées

1Affiliations récentes

Les institutions déclarées

Superior University

Les publications récentes

Accès ouvert 2026 article OpenAlex

Do Quantum Kernels Improve Medical Image Classification? A Leakage-Controlled Benchmark Against Classical RBF-SVM

Dr. M. Usman Hashmi, M. Adnan Hashmi, Muazzam Ali, Raheem Sarwar

Abstract Quantum kernel methods are often proposed as a route to improved medical image classification because quantum feature maps can embed data into high-dimensional Hilbert spaces. However, their practical value remains unclear when compared with strong classical kernels under leakage-controlled conditions. This …

ae, pk, gb (code pays fourni par la source)

0 citations Journal of Imaging Informatics in Medicine

Accès ouvert 2026 dataset OpenAlex

Are Hybrid Quantum Medical Image Classifiers Robust to NISQ Noise? A Leakage-Controlled Calibration and Finite-Shot Audit

Dr. M. Usman Hashmi, Dr. M. Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This repository contains the supplementary material, detailed experimental results, statistical analyses, and reproducibility files supporting the study “Are Hybrid Quantum Medical Image Classifiers Robust to NISQ Noise? A Leakage-Controlled Calibration and Finite-Shot Audit.” The study investigates the robustness of hybrid quantum–classical medical …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Are Hybrid Quantum Medical Image Classifiers Robust to NISQ Noise? A Leakage-Controlled Calibration and Finite-Shot Audit

Dr. M. Usman Hashmi, Dr. M. Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This repository contains the supplementary material, detailed experimental results, statistical analyses, and reproducibility files supporting the study “Are Hybrid Quantum Medical Image Classifiers Robust to NISQ Noise? A Leakage-Controlled Calibration and Finite-Shot Audit.” The study investigates the robustness of hybrid quantum–classical medical …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Do Quantum Kernels Improve Medical Image Classification A Leakage-Controlled Benchmark against Classical RBF-SVM

Dr. M. Usman Hashmi, M. Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This Zenodo record contains supporting research materials associated with the manuscript “Do Quantum Kernels Improve Medical Image Classification? A Leakage-Controlled Benchmark against Classical RBF-SVM,” accepted for publication in the Journal of Imaging Informatics in Medicine. The study presents a leakage-controlled comparison of …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Does Feature Compression Limit Hybrid Quantum Medical Image Classification? A Leakage-Controlled Classical-to-Quantum Bottleneck Study

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali

This reproducibility package supports the manuscript: “Does Feature Compression Limit Hybrid Quantum Medical Image Classification? A Leakage-Controlled Classical-to-Quantum Bottleneck Study” The study evaluates the classical-to-quantum feature compression bottleneck in hybrid quantum medical image classification using BreastMNIST and PneumoniaMNIST from MedMNIST v2. A …

pk, ae (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Does Feature Compression Limit Hybrid Quantum Medical Image Classification? A Leakage-Controlled Classical-to-Quantum Bottleneck Study

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali

This reproducibility package supports the manuscript: “Does Feature Compression Limit Hybrid Quantum Medical Image Classification? A Leakage-Controlled Classical-to-Quantum Bottleneck Study” The study evaluates the classical-to-quantum feature compression bottleneck in hybrid quantum medical image classification using BreastMNIST and PneumoniaMNIST from MedMNIST v2. A …

pk, ae (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Can Quantum Encoding Choice Close the Classical–Quantum Performance Gap in Medical Image Classification? A Controlled Five-Encoding Ablation

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This repository contains the supplementary material and reproducibility package for the manuscript “Can Quantum Encoding Choice Close the Classical–Quantum Performance Gap in Medical Image Classification? A Controlled Five-Encoding Ablation.” The study evaluates five quantum encoding strategies for variational quantum classifiers in a …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Can Quantum Encoding Choice Close the Classical–Quantum Performance Gap in Medical Image Classification? A Controlled Five-Encoding Ablation

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This repository contains the supplementary material and reproducibility package for the manuscript “Can Quantum Encoding Choice Close the Classical–Quantum Performance Gap in Medical Image Classification? A Controlled Five-Encoding Ablation.” The study evaluates five quantum encoding strategies for variational quantum classifiers in a …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Do Hybrid Classical–Quantum Models Offer a Reliable Advantage in Medical Image Classification? A Leakage-Controlled and Explainability-Aware Evaluation

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali, H.M; Shahzad Dar

This dataset contains the verified supplementary results supporting the manuscript “Do Hybrid Classical–Quantum Models Offer a Reliable Advantage in Medical Image Classification? A Leakage-Controlled and Explainability-Aware Evaluation.” The study evaluates hybrid classical–quantum machine-learning approaches for medical image classification using BreastMNIST, PneumoniaMNIST, and …

pk, ae (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Do Hybrid Classical–Quantum Models Offer a Reliable Advantage in Medical Image Classification? A Leakage-Controlled and Explainability-Aware Evaluation

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali, H.M; Shahzad Dar

This dataset contains the verified supplementary results supporting the manuscript “Do Hybrid Classical–Quantum Models Offer a Reliable Advantage in Medical Image Classification? A Leakage-Controlled and Explainability-Aware Evaluation.” The study evaluates hybrid classical–quantum machine-learning approaches for medical image classification using BreastMNIST, PneumoniaMNIST, and …

pk, ae (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Does a Quantum Kernel Classifier Degrade Less Under Chest X-ray Preprocessing Shift? A Leakage-Controlled Classical–Quantum Benchmark

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This repository contains the reproducibility materials supporting the manuscript “Do Quantum Kernels Degrade Less Under Chest X-ray Preprocessing Shift? A Leakage-Controlled Cross-Pipeline Benchmark.” The archive contains the complete 468-run experiment results, configuration-level and image-level prediction outputs, statistical-analysis outputs, experimental notebook, derived ResNet18 …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

Accès ouvert 2026 dataset OpenAlex

Does a Quantum Kernel Classifier Degrade Less Under Chest X-ray Preprocessing Shift? A Leakage-Controlled Classical–Quantum Benchmark

Usman Hashmi, Muhammad Adnan Hashmi, Muazzam Ali, Raheem Sarwar

This repository contains the reproducibility materials supporting the manuscript “Do Quantum Kernels Degrade Less Under Chest X-ray Preprocessing Shift? A Leakage-Controlled Cross-Pipeline Benchmark.” The archive contains the complete 468-run experiment results, configuration-level and image-level prediction outputs, statistical-analysis outputs, experimental notebook, derived ResNet18 …

pk, ae, gb (code pays fourni par la source)

0 citations Zenodo (CERN European Organization for Nuclear Research)

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