# Modeling Feature Maps for Quantum Machine Learning

arXiv: https://arxiv.org/abs/2501.08205 · HTML: https://mlsanity.com/paper/2501.08205/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [Adapting Quantum Machine Learning for Energy Dissociation of Bonds](https://mlsanity.com/paper/2510.06563/) (2025)
- [Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection](https://mlsanity.com/paper/2512.01365/) (2025)
- [Lane-Frame Quantum Multimodal Driving Forecasts for the Trajectory of Autonomous Vehicles](https://mlsanity.com/paper/2511.17675/) (2025)
- [Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection](https://mlsanity.com/paper/2511.21932/) (2025)
- [Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding](https://mlsanity.com/paper/2606.28655/) (2026)
- [On Evaluating Quantum Kernel Robustness for Low-Resource Cross-Corpus Audio Deepfake Detection](https://mlsanity.com/paper/2610.00649/) (2026)

## Abstract (arXiv)

Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devices poses practical challenges. This study systematically evaluates how various quantum noise models including dephasing, amplitude damping, depolarizing, thermal noise, bit-flip, and phase-flip affect key QML algorithms (QSVC, Peg-QSVC, QNN, VQC) and feature mapping techniques (ZFeatureMap, ZZFeatureMap, and PauliFeatureMap). Results indicate that QSVC is notably robust under noise, whereas Peg-QSVC and QNN are more sensitive, particularly to depolarizing and amplitude-damping noise. The PauliFeatureMap is especially vulnerable, highlighting difficulties in maintaining accurate classification under noisy conditions. These findings underscore the critical importance of feature map selection and noise mitigation strategies in optimizing QML for genomic classification, with promising implications for personalized medicine.