论文标题
量子状态的视觉配置分割用于多体系统中的相位识别
Visual configuration segmentation of quantum states for phase identification in many-body systems
论文作者
论文摘要
人工智能提供了一个前所未有的观点，用于研究凝结物系统中物质的阶段。图像分割是属于人工智能分支的计算机视觉的基本技术。在这项工作中，我们提出了一个名为“视觉构型分割（VC）”的分割方案，以在多体系统中揭示量子相和量子相变。通过将重新归一化的量子状态的信息编码到颜色图像中，并通过VC分割颜色图像，可以看到重新归一化的量子状态，可以从中揭示量子相变，并可以识别相应的临界点。我们的方案在几个强相关的自旋系统上进行了基准测试，这并不取决于量子相的顺序参数的先验知识。这证明了通过计算机视觉技术披露量子阶段的潜在结构的潜力。
Artificial intelligence provides an unprecedented perspective for studying phases of matter in condensed-matter systems. Image segmentation is a basic technique of computer vision that belongs to a branch of artificial intelligence. In this work, we propose a segmentation scheme named visual configuration segmentation (VCS) to unveil quantum phases and quantum phase transitions in many-body systems. By encoding the information of renormalized quantum states into a color image and segmenting the color image through the VCS, the renormalized quantum states can be visualized, from which quantum phase transitions can be revealed and the corresponding critical points can be identified. Our scheme is benchmarked on several strongly correlated spin systems, which does not depend on the priori knowledge of order parameters of quantum phases. This demonstrates the potential to disclose the underlying structure of quantum phases by the techniques of computer vision.
