Author

Daniel K. Park

2 papers indexed here

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Preprint Oct 2026

Prototype-Guided Optimization of Quantum Data Embeddings with Linear Cost

In supervised quantum machine learning with classical data, classification performance critically depends on the geometry induced by quantum data embedding. Existing data-adaptive approaches rely on pairwise fidelity evaluations between samples, which scale as $O(B^2)$ for $B$ samples and create a computational bottlen...

Seokhoon Jeong, Daniel K. Park · 0 citations

Review Aug 2026

Machine Learning Approaches to Decoding Topological Quantum Codes

This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations.

Changwon Lee, Tak Hur, Jeongwoo Jae et al. · 0 citations