Various Hamiltonian simulation algorithms have been proposed to efficiently study the dynamics of quantum systems on a quantum computer. The existing algorithms generally approximate the time evolution operators, which may need a deep quantum circuit that is beyond the capability of near-term noisy quantum devices. Here, focusing on the time evolution of a fixed input quantum state, we propose an adaptive approach to construct a low-depth time evolution circuit. By introducing a measurable quantifier that characterizes the simulation error, we use an adaptive strategy to learn the shallow quantum circuit that minimizes that error. We numerically test the adaptive method with electronic Hamiltonians of the $\mathrm{H_2O}$ and $\mathrm{H_4}$ molecules, and the transverse field Ising model with random coefficients. Compared to the first-order Suzuki-Trotter product formula, our method can significantly reduce the circuit depth (specifically the number of two-qubit gates) by around two orders while maintaining the simulation accuracy. We show applications of the method in simulating many-body dynamics and solving energy spectra with the quantum Krylov algorithm. Our work sheds light on practical Hamiltonian simulation with noisy-intermediate-scale-quantum devices.
Efficient Quantum Circuit Construction of Controlled Time-Evolution for Arbitrary Pauli-Sum Hamiltonians
Faculty of Engineering, Yokohama National University（横滨国立大学工学部）；Department of Applied Physics and Physico-Informatics, Keio University（庆应义塾大学应用物理·物理信息系）
Circuit Depth Reduction for Executable Hamiltonian Dynamics of Covalent Inhibitor Reactivity on Quantum Hardware
Capgemini Quantum Lab（凯捷量子实验室）；IBM T. J. Watson Research Center（IBM T.J.沃森研究中心）；Haiqu Inc.（海奇公司）；IBM Quantum IBM France Lab（IBM量子法国实验室）；GSK Medicines Research Centre（葛兰素史克药物研究中心）
Toward Optimal Circuit Depth for Geometrically Local Hamiltonian Simulation
Center on Frontiers of Computing Studies, School of Computer Science, Peking University（北京大学计算机科学学院前沿计算研究中心）；School of Physics, Peking University（北京大学物理学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学软件新技术国家重点实验室）；Hefei National Laboratory（合肥国家实验室）；Mathematical Institute, University of Oxford（牛津大学数学研究所）
Fluctuation-guided adaptive random compiler for Hamiltonian simulation
South China Normal University（华南师范大学）；Key Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education)（原子与亚原子结构及量子控制教育部重点实验室）；Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter（广东省物质结构与基本相互作用基础研究卓越中心）；School of Physics, South China Normal University（华南师范大学物理学院）；Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials（广东省量子工程与量子材料重点实验室）；Guangdong-Hong Kong Joint Laboratory of Quantum Matter（粤港澳量子物质联合实验室）；Frontier Research Institute for Physics, South China Normal University（华南师范大学前沿物理研究院）
