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arXiv · 2606.24932

Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation

Samuel Yen-Chi Chen·Yifeng Peng·Jiun-Cheng Jiang·Chun-Hua Lin·Kuo-Chung Peng·Junghoon Justin Park·Huan-Hsin Tseng·Hsin-Yi Lin·Kuan-Cheng Chen·Chen-Yu Liu·Shinjae Yoo
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Abstract

Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing. In this paper, we propose a Recursive Quantum Long Short-Term Memory model, or Recursive QLSTM, which extends QLSTM through metacore-based recursive constructions. We numerically test the model under different input sequence lengths, metacore designs, and recursive rules, and identify the best-performing architecture among these variants. For this selected model, we further provide theoretical arguments explaining why its recursive structure improves temporal information propagation and enhances learning performance. Our results suggest that Recursive QLSTM offers a flexible and effective framework for quantum recurrent learning over input time series of various lengths.

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quant-phcs.AIcs.ETcs.LGcs.NE

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BibTeXRIS

Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Kuo-Chung Peng, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo. 2026-06-22. Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation. https://arxiv.org/abs/2606.24932

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