论文标题
使用平行计算的Clifford转换同时对对角线化对哈密顿动力学的快速经典模拟
Fast Classical Simulation of Hamiltonian Dynamics by Simultaneous Diagonalization Using Clifford Transformation with Parallel Computation
论文作者
论文摘要
模拟量子多体动力学对于对物理学的基本理解和用于量子信息处理的实际应用都是重要的。因此,到目前为止已经开发了经典的仿真方法。具体而言,如果矩形数量足够小,以便主存储器可以存储状态向量,则Trotter-Suzuki分解可以分析高度复杂的量子动力学。但是,通过Trotter-Suzuki分解对量子动力学进行仿真需要大量步骤,每个步骤访问状态向量,因此模拟时间不切实际。为了解决这个问题,我们提出了一种技术,通过同时对互通上的Pauli群体进行对角化来加速量子动力学的模拟,这也引起了很多关注以减少量子算法中的测量开销。我们将汉密尔顿人分组为相互吸引的Pauli字符串,并且每个字符串都是通过Clifford变换在计算基础上对角度归对的。由于对角线运算符同时应用于状态矢量,并在最小内存访问中应用,因此该方法成功地使用了高度并行处理器(例如图形处理单元(GPU))的性能。与使用量子计算机最快的模拟器之一的实现相比,数值实验表明我们的方法提供了几十倍的加速度。
Simulating quantum many-body dynamics is important both for fundamental understanding of physics and practical applications for quantum information processing. Therefore, classical simulation methods have been developed so far. Specifically, the Trotter-Suzuki decomposition can analyze a highly complex quantum dynamics, if the number of qubits is sufficiently small so that main memory can store the state vector. However, simulation of quantum dynamics via Trotter-Suzuki decomposition requires huge number of steps, each of which accesses the state vector, and hence the simulation time becomes impractically long. To settle this issue, we propose a technique to accelerate simulation of quantum dynamics via simultaneous diagonalization of mutually commuting Pauli groups, which is also attracting a lot of attention to reduce the measurement overheads in quantum algorithms. We group the Hamiltonian into mutually commputing Pauli strings, and each of them are diagonalized in the computational basis via a Clifford transformation. Since diagonal operators are applied on the state vector simultaneously with minimum memory access, this method successfully use performance of highly parallel processors such as Graphics Processing Units (GPU). Compared to an implementation using one of the fastest simulators of quantum computers, the numerical experiments have shown that our method provides a few tens of times acceleration.