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quant-ph2026

Moment-Structured Block Encodings of Periodic Finite-Difference Operators

Jishnu Mahmud, Rebekah Herrman

The paper presents a framework for constructing optimal block encodings of translation‑invariant finite‑difference operators on periodic grids, linking stencil moment order to the…

quant-ph2026

Explicit Block Encoding of Difference-of-Gaussian Operators on a Periodic Grid

Jishnu Mahmud, John Winship, Tom Lash +2

The Difference-of-Gaussian (DoG) is a widely used operator across applications, including image processing (feature and edge detection), quantum machine learning, and finite-differ…

quant-ph2025

Hybrid Quantum-Classical Learning for Multiclass Image Classification

Shuchismita Anwar, Sowmitra Das, Muhammad Iqbal Hossain +1

This study explores the challenge of improving multiclass image classification through quantum machine-learning techniques. It explores how the discarded qubit states of Noisy Inte…

quant-ph2025

Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification

Shaswata Mahernob Sarkar, Sheikh Iftekhar Ahmed, Jishnu Mahmud +2

Quantum Machine Learning (QML) has seen significant advancements, driven by recent improvements in Noisy Intermediate-Scale Quantum (NISQ) devices. Leveraging quantum principles su…

quant-ph2024

Patch-Based End-to-End Quantum Learning Network for Reduction and Classification of Classical Data

Jishnu Mahmud, Shaikh Anowarul Fattah

In the noisy intermediate scale quantum (NISQ) era, the control over the qubits is limited due to the errors caused by quantum decoherence, crosstalk, and imperfect calibration. He…