Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines

• URL: http://arxiv.org/abs/2606.05387v1
• Date: Wed, 03 Jun 2026 19:46:35 GMT
• Title: Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines
• Abstract summary: The encoding of classical data into quantum states constitutes the primary performance bottleneck in Quantum Machine Learning.<n>No existing framework jointly characterises resource robustness, expressivity and noise robustness.<n>A five-regime decision framework maps (D, n, p, tau) feature dimension, qubit budget, error rate, and task type to a hardware-grounded encoding recommendation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The encoding of classical data into quantum states constitutes the primary performance bottleneck in Quantum Machine Learning (qml) on Noisy Intermediate-Scale Quantum (nisq) devices. No existing framework jointly characterises resource cost, expressivity, and noise robustness, nor provides actionable selection guidelines for practitioners. This survey addresses that gap through a systematic review of 66 primary works (2017-2026) assembled via a PRISMA-adapted protocol across five academic databases. Four principal contributions are made. First, a three-axis cost-expressivity-robustness taxonomy classifies all major encoding families - basis, angle, dense-angle, amplitude, data re-uploading, and IQP - along independently measurable axes. Second, closed-form depth-fidelity bounds under nisq decoherence channels identify the critical gate-error rate p* ~ 10^-3 below which amplitude encoding is viable. Third, a unified treatment of Fourier expressivity, barren-plateau onset, and quantum kernel concentration as functions of the encoding circuit provides the first joint trainability analysis. Fourth, a five-regime decision framework maps (D, n, p, tau) - feature dimension, qubit budget, error rate, and task type - to a hardware-grounded encoding recommendation. The central finding is that for p >= 10^-3, shallow angle-based encodings consistently outperform amplitude encoding in practice, despite the latter's exponential qubit advantage.

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