Breaking concentration barriers for quantum extreme learning on digital quantum processors
IBM Research Europe – Zurich（IBM 欧洲苏黎世研究所）；ETH Zürich（苏黎世联邦理工学院）；University of Basel（巴塞尔大学）；IBM Quantum, T. J. Watson Research Center（IBM 量子，T.J.沃森研究中心）；PINQ2；E.ON Digital Technology GmbH（E.ON 数字科技有限公司）
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad, Christophe Pere, Kumar Ghosh, Isabelle Wittmann, Thomas Brunschwiler, Giorgio Cortiana, Corey O'Meara, Stefan Woerner, Francesco Tacchino
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their practical applicability is hindered by hardware noise and concentration effects that can erase input-output distinguishability at large system sizes. In this work, we present and experimentally demonstrate a Quantum Extreme Learning Machine (QELM) tailored to state-of-the-art superconducting platforms, employing up to 124 qubits and circuits with more than 5,000 two-qubit gates on IBM Quantum computers. We introduce a practical multi-objective hyperparameter tuning strategy that jointly monitors observable variability, capacity, and task performance to identify noise-robust operating points. In addition, we develop a local eigentask analysis that enables computationally efficient feature selection and effective information retrieval. We report evidence of a regime of optimality that is identifiable at small scales and transferable across tasks and larger systems, and we achieve performances competitive with leading classical baselines on representative benchmarks for time-series forecasting and satellite image classification. Together, our results establish a viable and robust framework for large-scale, pre-fault-tolerant quantum machine learning and provide a foundation for extending reservoir-based methods to more expressive architectures and real-world scenarios.
Breaking the scalability barrier via a vertical tunable coupler in 3D integrated transmon system
National Laboratory of Solid State Microstructures, School of Physics, Nanjing University（南京大学固体微结构物理学院）；Shishan Laboratory, Nanjing University（南京大学时山实验室）；Jiangsu Key Laboratory of Ouantum Information Science and Technology, Nanjing University（南京大学江苏省量子信息科学与技术重点实验室）；Tencent Quantum Laboratory, Tencent（腾讯量子实验室）
Vibe Calibration: Autonomous Bring-up of a 112-Qubit Superconducting Quantum Processor by a Skill-Orchestrating Language Agent
Beijing Academy of Quantum Information Sciences（北京量子信息科学研究院）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；DP Technology（智谱AI）；Institute of Theoretical Physics, Chinese Academy of Sciences（中国科学院理论物理研究所）；Hangzhou Institute for Advanced Study, UCAS（中国科学院大学杭州高等研究院）
Impact of Oxygen Vacancies in Josephson Junction on Decoherence of Superconducting Qubits
College of Physics and Electronic Engineering, Center for Computational Sciences, Sichuan Normal University（四川师范大学物理电子工程学院计算科学中心）；Sichuan Province Key Laboratory of Optoelectronic Sensor Devices and Systems, College of Optoelectronic Engineering, Chengdu University of Information Technology（成都信息工程大学光电工程学院四川省光电传感器件与系统重点实验室）；Sichuan Meteorological Optoelectronic Sensor Technology and Application Engineering Research Center, Chengdu University of Information Technology（成都信息工程大学四川省气象光电传感器技术与应用工程研究中心）
