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
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
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:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.
By Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu
Qlippy is a retrieval‑augmented generative AI assistant designed to support reproducible quantum software development. It is embedded in the development environment and grounds its responses in a curated corpus of quantum‑software‑engineering knowledge, explaining reproducibility and provenance concepts in context. The assistant augments Qiskit programs with MLflow‑based experiment tracking that follows the QProv schema, thereby reducing reliance on large language models and enabling low‑cost, privacy‑preserving local deployment.
By Mahee Gamage, Vlad Stirbu
arXiv:2607. 25865v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing.
By Ge Yan, Shanchuan Li, Pengyue Ma, Qixin Zhang, Pingchuan Ma, Jianping Wang, Min-Hsiu Hsieh, Yuxuan Du
arXiv:2606. 29966v1 Announce Type: cross Abstract: Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features.
By Xindian Ma, Xinyu Long, Yefei Zhang, Yanchen Liu, Xianghao Li, Yufu Wen, Yike Hu, Yuedong Zhu, Zeyang Ma, Wen Qin, Yikun Wang, Peng Yang, Monan Wang, Teng Yu
arXiv:2603. 13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher.
By Haonan Huang
Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs.
arXiv:2609.25082v1 Announce Type: new
Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fr...
By Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria
The paper presents a reinforcement‑learning approach to schedule link‑level entanglement in quantum networks, using a Markov Decision Process and double deep Q‑networks with message‑passing neural networks. The resulting policies achieve 100% success rates even when the link activation probability is reduced by up to 71% compared to baseline heuristics, and maintain at least 80% success when task placements are hardware‑restricted. The authors also develop metrics to interpret the learned policy and employ a large language model to generate a heuristic that matches the DQN performance, suggesting a scalable method for extracting interpretable strategies in large quantum networks.
By Leon Rode, Sumeet Khatri, Supartha Podder