Learning many-body Hamiltonians with Heisenberg-limited scaling
California Institute of Technology（加州理工学院）；University of California, Berkeley（加州大学伯克利分校）；Simons Institute for the Theory of Computing（西蒙斯计算理论研究所）；Microsoft Quantum（微软量子）
Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this work, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting $N$-qubit local Hamiltonian. After a total evolution time of $\mathcal{O}(ε^{-1})$, the proposed algorithm can efficiently estimate any parameter in the $N$-qubit Hamiltonian to $ε$-error with high probability. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses $\mathrm{polylog}(ε^{-1})$ experiments. In contrast, the best previous algorithms, such as recent works using gradient-based optimization or polynomial interpolation, require a total evolution time of $\mathcal{O}(ε^{-2})$ and $\mathcal{O}(ε^{-2})$ experiments. Our algorithm uses ideas from quantum simulation to decouple the unknown $N$-qubit Hamiltonian $H$ into noninteracting patches, and learns $H$ using a quantum-enhanced divide-and-conquer approach. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.
Ansatz-free Hamiltonian learning with Heisenberg-limited scaling
Department of Physics, Harvard University, Cambridge, MA 02138, USA（哈佛大学物理系）；Institute for Quantum Information and Matter, California Institute of Technology, CA 91125, USA（加州理工学院量子信息与物质研究所）；School of Engineering and Applied Sciences, Harvard University, Allston, Massachusetts 02134, USA（哈佛大学工程与应用科学学院）；Center for Quantum Information, IIIS, Tsinghua University, Beijing, China（清华大学信息中心）；Department of Mathematics, Duke University, Durham, NC 27708, USA（杜克大学数学系）；Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA（杜克大学电气与计算机工程系）；Duke Quantum Center, Duke University, Durham, NC 27701, USA（杜克大学量子中心）；Department of Computer Science, Virginia Tech, Alexandria, VA 22314, USA（弗吉尼亚理工大学计算机科学系）；Phasecraft Inc., Washington DC, USA（Phasecraft公司）
Continuous Variable Hamiltonian Learning at Heisenberg Limit via Displacement-Random Unitary Transformation
School of Stomatology, Peking University（北京大学口腔医学院）；Department of Chemistry and Biochemistry, University of California, Los Angeles（加州大学洛杉矶分校化学与生物化学系）；Department of Physics, Tsinghua University（清华大学物理系）；Institute of Advanced Study, Tsinghua University（清华大学高等研究院）
Dynamical topological quantum computation using spin pulse control in the Heisenberg model
当合适的材料在自然界中不存在或不稳定时，哈密顿工程是量子信息处理的重要途径。到目前为止，还没有一个稳定的材料的Kitaev自旋哈密顿量与各向异性相互作用的蜂窝晶格（A。Kitaev，Annals of Physics vol 321，2（2006）），它在阿贝尔和非阿贝尔任意子的实现中起着至关重要的作用。在这里，我们展示了如何动态实现Kitaev自旋哈密顿量从传统的海森堡自旋哈密顿量使用脉冲控制技术。通过重复相同的脉冲序列，动态地保持量子态。研究了自旋-轨道相互作用和超精细相互作用的影响。
Heisenberg-Limited Quantum Hamiltonian Learning via Randomly Spread Product-States
ICFO - The Institute of Photonic Sciences（光子科学研究所）；Forschungszentrum Jülich, Institute of Quantum Control, Peter Grünberg Institut (PGI-8)（于利希研究中心量子控制研究所彼得·格林伯格研究所）；Quside Technologies SL（Quside科技有限公司）
Quantum antiferromagnetic Heisenberg half-odd integer spin model as the entanglement Hamiltonian of the integer spin Affleck-Kennedy-Lieb-Tasaki states
Heisenberg-limited Hamiltonian learning without short-time control
School of Computational Sciences, Korea Advanced Institute of Science and Technology（韩国科学技术院计算科学学院）；Institute of Computer Technology, Seoul National University（首尔大学计算机技术研究所）；Department of Physics, Korea Advanced Institute of Science and Technology（韩国科学技术院物理系）
Quantum Critical Dynamics of a Heisenberg-Ising Chain in a Longitudinal Field: Many-Body Strings versus Fractional Excitations
利用高分辨率太赫兹光谱研究了反铁磁Heisenberg-Ising自旋链化合物BaCo $_2$ V $_2$ O $_8$的量子自旋动力学随温度和纵向磁场的变化.在低于$T_N\simeq 5.5$ K的反铁磁相中观察到了受限的自旋激发。在一个以上的$B_c=3.8$ T的场诱导的无带隙阶段，我们确定多体弦激发以及低能量分数psinon/antipsinon激发通过比较Bethe-Anglesia计算。在$B_c$附近，高能量弦激发被发现是动态占主导地位的分数激发。
Heisenberg and Heisenberg-Like Representations via Hilbert Space Bundle Geometry in the Non-Hermitian Regime
National Sun Yat-sen University（国立中山大学）；National Center for Theoretical Sciences（国家理论科学中心）；Adam Mickiewicz University（亚当·密茨凯维奇大学）；RIKEN（理化学研究所）；Basque Center for Applied Mathematics（巴斯克应用数学中心）；National Chung Hsing University（国立中兴大学）；University of Michigan（密歇根大学）
Generalized $m$-body mean-field projections for classical simulation of $d$-dimensional Heisenberg-picture dynamics
Universität Ulm（乌尔姆大学）；Center for Integrated Quantum Science and Technology (IQST)（集成量子科学与技术中心）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Instituto de Física La Plata (IFLP), CONICET–Universidad Nacional de La Plata（拉普拉塔物理研究所，阿根廷国家科学技术研究委员会-拉普拉塔国立大学）
Learning Inhomogeneous Heisenberg Hamiltonians in Nanographene Spin Chains
Aalto University（阿尔托大学）；Empa—Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科技与检测局）；Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area（粤港澳大湾区量子科学中心）；Max Planck Institute of Microstructure Physics（马克斯·普朗克微结构物理研究所）；Technische Universität Dresden（德累斯顿工业大学）
