2022年，Advanced Intelligent Systems和Advanced Quantum Technologies分别获得首个影响因子7.298和5.31。Wiley将举行一系列在线研讨会，在人工智能、量子技术及其交叉领域展开学术交流，期待激发出新的思维火花，为科学技术的发展贡献期刊力量。
第一场专场研讨会，来自新加坡、西班牙和中国科研机构的三位科学家将就人工智能辅助热电器件设计、量子机器学习、量子操纵及其在量子信息中的应用等问题展开分享与讨论。
2022年7月12日 （星期二） 晚19:00 - 21:00（北京时间）
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Advanced Quantum Technologies 期刊简介
Advanced Intelligent Systems 期刊简介
Artificial Intelligence Assisted Design of Topological and Thermoelectric Devices
张刚 教授
新加坡高性能计算研究院研究室主任、高级科学家
Quantum Machine Learning with Quantum
Quantum Steering and its Applications in Quantum Information
张刚博士是英国物理学会IOP Fellow，分别于1998年和2002年获得清华大学物理学学士和博士学位。张刚博士于2013年2月加入新加坡高性能计算研究所(IHPC)，目前是该研究所的高级科学家和研究室主任。在加入IHPC之前，他曾任北京大学电子系的教授。张刚教授在纳米级热传导和纳米材料的各种应用方面做出了突破性的贡献，并发表了280多篇论文，被引次数超过21000次，h-index为75。
报告主题: Artificial Intelligence Assisted Design of Topological and Thermoelectric Devices
报告摘要：
Thermoelectric (TE) materials provide a solid-state solution in waste heat recovery and refrigeration. For a TE material, its conversion efficiency is characterized by a dimensionless quantity called figure of merit ZT. ZT can be expressed in terms of the electric conductance, the Seebeck coefficient and the thermal conductance. During the past few decades, considerable effort has been devoted for improving the performance of TE materials. Currently, there is still a challenge facing TE devices: Both heat-work conversion efficiency and output power are low. The difficulty in improving ZT comes from the fact that these transport coefficients are generally closely related to each other. Machine learning methods, well-known for their data analysis capability, have been successfully applied to research on TE materials in recent years. Here the speaker presents their recent works of the artificial intelligence assisted design of TE materials and other quantum materials.
Lucas Lamata教授是西班牙塞维利亚大学的副教授，他的研究包括用捕获离子和超导电路进行量子模拟，量子人工智能和量子机器学习，以及用量子可控系统模拟生物行为。Lucas Lamata教授在包括Nature、Advanced Quantum Technologies、Physical Review Letters等国际期刊上发表了110多篇文章，h-index为40，引用次数超过6200次。
报告主题: Quantum Machine Learning with Quantum Technologies
报告摘要：
I will give an overview of the emerging field of quantum machine learning, its motivations, and prospects. I will then review a few recent papers on one specific kind of quantum machine learning algorithm: quantum reinforcement learning, for state estimation and eigensolver optimization with current quantum technologies. Finally, I will describe recent developments on photonic quantum memristors.
何琼毅教授自2012年开始在北京大学物理学院现代光学所工作，2002年本科毕业于东北师范大学物理系，2007年在吉林大学物理系获得博士学位，之后在澳大利亚昆士兰大学和斯威本科技大学做博士后。何琼毅教授是国家杰出青年科学基金获得者。
报告主题: Quantum Steering and its Applications in Quantum Information
报告摘要：
The concept of quantum steering was originally introduced by Schrödinger to describe the “spooky action-at-a-distance” effect noted in the Einstein-Podolsky-Rosen (EPR) paradox, whereby local measurements performed on one party apparently adjust (steer) the state of another distant party. We deeply explore the characteristics of bipartite and multipartite steering to establish what usefulness to quantum communication protocols can such a resource provide, where bare entanglement is not enough, and Bell nonlocality may not be accessible. I will give an overview of our recent developments on quantum steering and its applications in quantum information.
Advanced Intelligent Systems
Wiley旗下智能系统领域开放获取旗舰刊。期刊收录关于具有刺激或指令响应智能的人造装置系统的研究，包括机器人、自动化、人工智能、机器学习、人机交互、智能传感和程序化自组装等前沿应用。
Advanced Quantum Technologies
Wiley Advanced 家族量子领域旗舰刊，旨在发表经同行评议的高质量高影响力研究论文，范围涵盖量子计算、量子通讯、量子信息、量子光学以及拓扑材料、超导、超冷原子等相关领域的理论、实践及应用相关工作。
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