EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data

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.

@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 }
}