Author

Muhammad Faryad

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Review Aug 2026

The Input Problem: A Permanent Bottleneck for Quantum Machine Learning

It is explained that the efficiently preparable states, device-generated distributions, variationally learned loading, and amortized preparation are required to get advantage from quantum machine learning and close with a checklist for evaluating input-dependent advantage claims.

M. Faryad · 0 citations

Preprint Aug 2026

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

It is shown that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring does not.

Muhammad Faryad · 0 citations