论文标题
通过使用神经网络波函数，相关比和水平光谱法，通过精制的量子多体求解器揭示了Dirac-type节点自旋液体
Dirac-type nodal spin liquid revealed by refined quantum many-body solver using neural-network wave function, correlation ratio, and level spectroscopy
论文作者
论文摘要
追求不承受常规可测量物体特性的分数化颗粒，例如电子和诸如元音等基本激发的裸粒子，是物理学的挑战。在这里，我们表明，一种用于达到最先进准确性的量子多体系统的机器学习方法表明，该区域中存在量子旋转液体（QSL）阶段$ 0.49 \ lisssim j_2/j_1 \ simsim0.54 \ simsim0.54 $ sisten和jeisenberg yexpients $ j_ jeys $ j_ $ j_ $ j_ $ j_ $ j_ $ j_ $ j_ $ j_ $ $在方格上。这是通过与称为相关率和水平光谱方法的尖端计算方案结合来实现的，以减轻有限尺寸的效果。基态相关性与激发光谱之间的相关性之间的定量对应关系可以使QSL及其性质的可靠识别和估计。包含单线和三胞胎的自旋激发光谱，类似于无间隙的dirac样色散，表明了无间隙分离的旋转1/2 dirac-type旋子在独特的QSL相中的出现。揭示了néel抗磁磁性和二聚体相关性的共存和双重幂律衰减的未开发临界行为。两个相关的幂律衰减指数在QSL阶段与$ J_2/j_1 $不同，因此具有不同的值，除了满足两个相关的对称性的单点。激发与Cuprate $ d $ - 波超导体的激发同构意味着当前的QSL与超导性之间存在紧密的联系。这项成就表明，使用机器学习技术的量子状态表示，主要仅限于基准，是研究量子多体物理学挑战的有前途的工具。
Pursuing fractionalized particles that do not bear properties of conventional measurable objects, exemplified by bare particles in the vacuum such as electrons and elementary excitations such as magnons, is a challenge in physics. Here we show that a machine-learning method for quantum many-body systems that has achieved state-of-the-art accuracy reveals the existence of a quantum spin liquid (QSL) phase in the region $0.49\lesssim J_2/J_1\lesssim0.54$ convincingly in spin-1/2 frustrated Heisenberg model with the nearest and next-nearest neighbor exchanges, $J_1$ and $J_2$, respectively, on the square lattice. This is achieved by combining with the cutting-edge computational schemes known as the correlation ratio and level spectroscopy methods to mitigate the finite-size effects. The quantitative one-to-one correspondence between the correlations in the ground state and the excitation spectra enables the reliable identification and estimation of the QSL and its nature. The spin excitation spectra containing both singlet and triplet gapless Dirac-like dispersions signal the emergence of gapless fractionalized spin-1/2 Dirac-type spinons in the distinctive QSL phase. Unexplored critical behavior with coexisting and dual power-law decays of Néel antiferromagnetic and dimer correlations is revealed. The power-law decay exponents of the two correlations differently vary with $J_2/J_1$ in the QSL phase and thus have different values except for a single point satisfying the symmetry of the two correlations. The isomorph of excitations with the cuprate $d$-wave superconductors implies a tight connection between the present QSL and superconductivity. This achievement demonstrates that the quantum-state representation using machine learning techniques, which had mostly been limited to benchmarks, is a promising tool for investigating grand challenges in quantum many-body physics.
