论文标题

使用量子张量网络对热状态的Q量子仿真

Qubit-efficient simulation of thermal states with quantum tensor networks

论文作者

Zhang, Yuxuan, Jahanbani, Shahin, Niu, Daoheng, Haghshenas, Reza, Potter, Andrew C.

论文摘要

我们提出了一种全息量子仿真算法,以变化准备$ d $二维相互作用的量子多体系统的热状态,仅使用足够的硬件量子台来表示($ d $ -1)尺寸横截面。该技术通过大约将量子矩阵密度算子(QMPDO)大约挖掘到量子矩阵量态(STO-QMP)的随机混合物中来实现热状态。生成QMP的量子电路的参数和生成随机混合物的概率分布的参数是通过各种优化过程确定的。我们展示了该技术在Quantinuum的被困量子处理器上的原理证明的小规模证明,以模拟仅使用一对硬件零件的宽温度范围内相关的自旋链的热性能。然后,通过经典模拟,我们探索了两个版本的Sto-QMPS Ansatzes的代表力,以用于更大,更深的电路,并在电路资源和变异自由能的准确性之间建立经验关系。

We present a holographic quantum simulation algorithm to variationally prepare thermal states of $d$-dimensional interacting quantum many-body systems, using only enough hardware qubits to represent a ($d$-1)-dimensional cross-section. This technique implements the thermal state by approximately unraveling the quantum matrix-product density operator (qMPDO) into a stochastic mixture of quantum matrix product states (sto-qMPS). The parameters of the quantum circuits generating the qMPS and of the probability distribution generating the stochastic mixture are determined through a variational optimization procedure. We demonstrate a small-scale proof of principle demonstration of this technique on Quantinuum's trapped-ion quantum processor to simulate thermal properties of correlated spin-chains over a wide temperature range using only a single pair of hardware qubits. Then, through classical simulations, we explore the representational power of two versions of sto-qMPS ansatzes for larger and deeper circuits and establish empirical relationships between the circuit resources and the accuracy of the variational free-energy.

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