arXiv:2608. 07743v1 Announce Type: new Abstract: Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope.
By Yijing Zuo, Zhe Fu, Zihan Nie, Zhihui Zhu, Haohan Wang
arXiv:2607. 25834v1 Announce Type: cross Abstract: Quantum computers are moving from research laboratories to industrial machines accessible via the cloud and integrated into high-performance computing facilities.
By Constantin Dalyac, Alexandre Dauphin, Lo\"ic Henriet, Christophe Jurczak
The paper introduces Quantum‑Harbor, a virtual laboratory that lets AI agents interact with quantum systems in a controlled setting, enabling verification of their actions and conclusions. Using this platform, the authors created QIQCBench, a benchmark of 49 expert‑authored tasks covering calibration, control, error correction, compilation, sensing, and networking. Testing 17 state‑of‑the‑art agentic systems on QIQCBench revealed wide variation in verified performance, highlighting a gap between demonstrated capability and reliable operation and positioning Quantum‑Harbor as a foundation for measuring progress toward verified autonomy in quantum engineering.
By Naixu Guo, Changhao Li, Siyu Cheng, Qicheng Tang, Binzhao Luo, Bikun Li, Yuxuan Du, Shihao Ru, Jiaqi Cai
MIT Quantum Initiative (QMIT) has launched a new postdoctoral fellowship program, welcoming its first cohort of QMIT Fellows this fall. The initiative aims to advance interdisciplinary quantum research by supporting early-career scientists in collaborative projects across multiple fields.
By Liam McDonnell | Office of Innovation and Strategy
arXiv:2605. 30358v2 Announce Type: replace Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise.
By Zhenxiao Fu, Lei Jiang, Fan Chen
The paper introduces Hybrid Quantum Root Cause Analysis (HQ‑RCA), a workflow that applies a hybrid Quantum Graph Neural Network (QGNN) to banking IT operations. HQ‑RCA replaces the classification head of the classical DynEdge GNN with a Variational Quantum Circuit, achieving comparable F1 performance to the strongest classical baseline while simplifying the quantum readout to a single Pauli‑Z expectation. Experiments on 13 k anonymised alarm clusters from a major European bank demonstrate that the quantum component can be executed on NISQ hardware without error mitigation, using a gradient‑free grid scan for optimisation.
By Antonio Greco, Riccardo Paoletti, Roberto Cappuccio, Mario Onorato