arXiv:2607. 27945v1 Announce Type: cross Abstract: Sequence models must decide what to write into memory and what to retain.
By Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Tzung-Chi Huang, Prayag Tiwari, Chi-Sheng Chen, Chun-Hua Lin, Yu-Chao Hsu, Tai-Yue Li, Saif Al-Kuwari, Simon See, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan
arXiv:2606. 24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing.
By Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo
arXiv:2605. 06734v2 Announce Type: replace-cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states.
By Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Prayag Tiwari, Andrea Ceschini, Chi-Sheng Chen, Yu-Chao Hsu, Chun-Hua Lin, Tai-Yue Li, Antonello Rosato, Massimo Panella, Simon See, Saif Al-Kuwari, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan
arXiv:2605. 12713v3 Announce Type: replace-cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed.
By Erik L. Connerty, Ethan N. Evans
arXiv:2607. 16358v1 Announce Type: cross Abstract: This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F).
By Sanjay Chakraborty, Fredrik Heintz
The paper introduces a recursive quantum long short-term memory (QLSTM) architecture and compares it to a standard QLSTM for one-step-ahead daily temperature forecasting. Using Toronto weather data and identical training settings, the recursive model consistently reaches near-optimal test loss earlier, achieves lower mean absolute error and root mean squared error, and shows a smaller generalization gap across input windows of 8, 16, and 32 days over 20 random seeds. These findings suggest that recursive quantum feature transformations can enhance stability and out-of-sample performance in compact hybrid quantum–classical temporal models.
By Mu-En Lee, Yen-Ku Liu, Samuel Yen-Chi Chen, Yun-Cheng Tsai