The paper presents an autonomous research laboratory where large‑language‑model agents collaborate to formulate, evaluate, and experimentally test research questions. In a study of quantum spins in diamond, the agents determined the minimal calibration settings for predicting microwave pulse responses, performed computational checks, and designed experiments to discriminate between competing predictions. When initial data drifted, the agents deemed the result inconclusive, revised the protocol, and ultimately confirmed the theoretical predictions with a successful experiment.
By El Mustapha Mansouri, Eikichi Kimura, Masato Koga, Keigo Arai
The article titled "The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry" discusses insights from the TPC26 conference, where leaders from academia, national laboratories, and industry examined how AI, autonomous agents, self-driving labs, high‑performance computing, and quantum computing converge to accelerate materials science discovery. It presents firsthand experiences from researchers at the forefront of these technologies and argues that their simultaneous maturation marks a tipping point for transformative advances and productive disruption in chemical sciences.
By Eliu Huerta, Xiaoyun Wang, Geetika Gupta, Edward H. Sargent, Cameron J. Owen, Victor Fung, Abhishek Mitra, Austin Cheng, Emma Bouchard, Shams Mehdi
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
The paper introduces AI Theorist, a system of AI agents that autonomously discovers physical models by generating hypotheses, performing first‑principles calculations, and refining them based on evidence. Applied to the quantum material α‑RuCl₃, the system interprets optical and photocurrent data, revealing distinct excitonic states with different optical selection rules and spatial distributions. This marks the first demonstration of an AI system autonomously developing a physical model to explain previously unpublished experimental observations in a quantum material using first‑principles and many‑body calculations.
By Hongjian Zhou, Xianfan Nie, Sean Wu, Tarun Patel, Jinge Wu, Andrew Liu, Adam Wei Tsen, David A. Clifton
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:2603.17043v2 Announce Type: replace
Abstract: Physics-aware multimodal large language models (MLLMs) can localize and characterize two-dimensional (2D) material flakes from optical microscopy i...
By Sankalp Pandey, Thanh-Dat Truong, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu
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
arXiv:2607. 25145v1 Announce Type: cross Abstract: We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond.
By Takuya Isogawa, Ryotaro Okabe, Nutdech Phadetsuwannukun, Mingda Li, Paola Cappellaro
HERA is an autonomous agent that performs cryogenic transport measurements on quantum devices using a curated knowledge base of protocols, software, and literature. In a blinded trial, HERA matched an expert’s conclusions on the stacking order of multilayer graphene devices, and ablation and replay studies demonstrated that the knowledge base improves measurement design and reasoning across models. When tasked with discovering superconductivity in a new material, HERA autonomously developed and executed the experiment after minimal expert input, illustrating its capability for sound experimental science.
By Zachary W. Gomez, Nathan Bittner, Jordan T. McCourt, Qingrui Cao, Sandesh S. Kalantre, Benjamin H. Alexander, Zhiren Zheng, Kenji Watanabe, Takashi Taniguchi, Aaron Sharpe
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 investigates quantum machine learning (QML) for magnetic-field estimation using nitrogen‑vacancy (NV) centers in diamond. By framing sensing as a supervised regression task, the authors compare classical machine learning models trained on measurement‑based data with quantum kernel‑based models trained on pre‑measurement coherent quantum states. They find that QML performance improves markedly when coherent quantum‑state information is available, whereas changes in model complexity or learning paradigm have little effect, highlighting the need for tightly integrated quantum‑sensor and QML pipelines.
By Sounak Bhowmik, Himanshu Thapliyal
arXiv:2610.15236v1 Announce Type: cross
Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization...
By Zhi-Xun Shen, Jin Gene Wong, Karen Lei, Leon Garcia, Felix Baumberger, Sergey Borisenko, Aaron Bostwick, Ting Cao, Tanny Chavez, Yulin Chen, Monika Choudhary, Andrea Damascelli, Dan Dessau, Thomas P. Devereaux, Sandy A. Ekahana, Alexei Fedorov, Atsushi Fujimori, Yucheng Guo, M. Zahid Hasan, Makoto Hashimoto, Yu He, Philip Hofmann, Zahid Hussain, Harold Y. Hwang, Zhurun Ji, Chunjing Jia, Timur K. Kim, Phil D. C. King, Takeshi Kondo, Alessandra Lanzara, Chun Lin, Donghui Lu, Matteo Michiardi, Sung-Kwan Mo, Brian Moritz, Laurenz Rettig, Eli Rotenberg, Takafumi Sato, Kyle M. Shen, Jonathan A. Sobota, Inna Vishik, Shuolong Yang, Ming Yi, Timothy Zaklama