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,...

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Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Han, Jason, DiBrita, Nicholas S., Cho, Younghyun, Luo, Hengrui, Patel, Tirthak
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Encoding
Error analysis
Integrated circuit modeling
Integrated circuit reliability
Logic gates
Machine learning
Noise
Noise level
Noise measurement
Quantum circuit
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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 94% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices. Our opensource solution provides a scalable and efficient alternative for integrating classical data with quantum models.
AbstractList 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 94% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices. Our opensource solution provides a scalable and efficient alternative for integrating classical data with quantum models.
Author DiBrita, Nicholas S.
Luo, Hengrui
Patel, Tirthak
Cho, Younghyun
Han, Jason
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  fullname: Luo, Hengrui
  organization: Rice University
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  givenname: Tirthak
  surname: Patel
  fullname: Patel, Tirthak
  organization: Rice University
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Snippet Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods...
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SubjectTerms Encoding
Error analysis
Integrated circuit modeling
Integrated circuit reliability
Logic gates
Machine learning
Noise
Noise level
Noise measurement
Quantum circuit
Title EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
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