arXiv:2607. 18552v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout.
By Shehbaz Tariq, Muhammad Talha, Arshid Ali, Muhammad Diyan, Symeon Chatzinotas
arXiv:2512. 06695v3 Announce Type: replace Abstract: Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data.
By Haipeng Cao, Kaining Zhang, Dacheng Tao, Zhaofeng Su
arXiv:2401. 07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in quantum information processing.
By Chuangtao Chen, Qinglin Zhao, MengChu Zhou, Zhimin He, Zhili Sun, Haozhen Situ
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. 07978v1 Announce Type: cross Abstract: Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it.
By Tushar Pandey
arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.
By Kung-Ming Lan