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quant-ph2026

Batched pattern search for QAOA parameter optimization on cloud-accessed quantum hardware

Muhammad Faryad

On a cloud-accessed quantum processor the classical optimizer of a variational algorithm communicates with the device through submitted jobs, and each job carries a queueing and tu…

quant-ph2026

Noise Resilience of Quantum Support Vector Machine with Selected Feature Maps

Muhammad Ahsan Shakeel, Saad Muzammil, Danyal Tayyub +1

Gate-level noise degrades the classification accuracy of Quantum Support Vector Machines (QSVMs) on Noisy Intermediate-Scale Quantum (NISQ) hardware, and the degree of degradation…

quant-ph2026

Quantum Kernel k-Means for Credit-Card Fraud Detection:A Controlled Benchmark on Real Transaction Data

Muhammad Faryad

We benchmarked quantum kernel -means against classical clustering for credit-card fraud detection on real transaction data, at up to \MaxQubits{} qubits, under a protocol with s…

quant-ph2026

The Input Problem: A Permanent Bottleneck for Quantum Machine Learning

Muhammad Faryad

Quantum algorithms are conventionally presented with their input state supplied for free. When the input is classical data, this convention conceals a cost that is frequently large…

quant-ph2026

A Systematic Study of Noise Effects in Hybrid Quantum-Classical Machine Learning

Bhavna Bose, Muhammad Faryad

Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of…

quant-ph2026

Hybrid Quantum--Classical k-Means Clustering via Quantum Feature Maps

Syed M. Abdullah, Alisha Baba, Muhammad Siddique +1

Clustering is one of the most fundamental tasks in machine learning, and the k-means clustering algorithm is perhaps one of the most widely used clustering algorithms. However, it…