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

• URL: http://arxiv.org/abs/2503.14473v1
• Date: Tue, 18 Mar 2025 17:48:03 GMT
• Title: EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
• Authors: Jason Han, Nicholas S. DiBrita, Younghyun Cho, Hengrui Luo, Tirthak Patel,
• Abstract summary: Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits.<n>We introduce EnQode, a fast AE technique based on symbolic representation that addresses limitations by clustering dataset samples.<n>With over 90% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices.
• Score: 4.329112531155235
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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