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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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Han, Jason, DiBrita, Nicholas S., Cho, Younghyun, Luo, Hengrui, Patel, Tirthak
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Encoding Error analysis Integrated circuit modeling Integrated circuit reliability Logic gates Machine learning Noise Noise level Noise measurement Quantum circuit
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