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

EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data Jason Han Rice University Houston, USA Nicholas S. DiBrita Rice University Houston, USA Y ounghyun Cho Santa Clara University Santa Clara, USA Hengrui Luo Rice University Houston, USA Tirthak Patel Rice University Houston, USA Abstract --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 SW AP 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. I NTRODUCTION As quantum computing advances toward broader applicability, one of its key challenges is interfacing classical data with quantum algorithms [40], [34]. Quantum machine learning (QML) has shown potential in fields ranging from material discovery to the physical sciences, with amplitude embedding (AE) being the critical mechanism for encoding classical data onto quantum states [11], [12], [25].