Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variable-length circuits that introduce high output error due to extensive gate usage and variable error rates across samples, resulting in noise-driven inconsistencies that degrade model accuracy. We introduce EnQode, a fast AE technique based on symbolic representation that addresses these limitations by clustering dataset samples and solving for cluster mean states through a low-depth, machine-specific ansatz. Optimized to reduce physical gates and SWAP operations, EnQode ensures all samples face consistent, low noise levels by standardizing circuit depth and composition. With over 90% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices. Our open-source solution provides a scalable and efficient alternative for integrating classical data with quantum models.
View on arXiv@article{han2025_2503.14473,
title={ EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data },
author={ Jason Han and Nicholas S. DiBrita and Younghyun Cho and Hengrui Luo and Tirthak Patel },
journal={arXiv preprint arXiv:2503.14473},
year={ 2025 }
}