虚拟现实技术在量子光学中的应用:了解人工智能驱动的科学发现

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2024-08-13 DOI:10.1088/2632-2153/ad5fdb
Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn
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引用次数: 0

摘要

人工智能(AI)生成模型可以为人类无法解决的科学问题提出解决方案。要真正在概念上有所贡献,研究人员需要有能力理解人工智能生成的结构,并提取潜在的概念和想法。当算法在输出的同时几乎不提供解释性推理时,科学家就必须仅根据示例来逆向工程建议背后的基本见解。这项任务极具挑战性,因为输出结果往往非常复杂,人类无法立即理解。在这项工作中,我们展示了如何将部分分析过程转移到沉浸式虚拟现实(VR)环境中,从而帮助研究人员理解人工智能生成的解决方案。我们展示了虚拟现实在寻找代表量子光学实验的抽象图形的可解释配置方面的实用性。因此,我们可以手动发现人工智能发现的新概括以及对量子光学实验的新理解。此外,它还允许我们在知情的情况下定制搜索空间--作为 "回路中的人",从而大大加快后续发现迭代的速度。举个具体例子,利用这项技术,我们发现了一种新的资源节约型三维纠缠交换方案,以及一种三维四粒子格林伯格-霍恩-蔡林格状态分析器。我们的研究结果表明,虚拟现实技术可以提高研究人员从基于图的生成式人工智能中获取知识的能力。这种人工智能是各科学领域广泛使用的一种抽象数据表示方式。
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Virtual reality for understanding artificial-intelligence-driven scientific discovery with an application in quantum optics
Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive virtual reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way—as a human-in-the-loop—to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger–Horne–Zeilinger-state analyzer. Our results show the potential of VR to enhance a researcher’s ability to derive knowledge from graph-based generative AI. This type of AI is a widely used abstract data representation in various scientific fields.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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