论文标题
重建20克量子模拟器的复杂状态
Reconstructing complex states of a 20-qubit quantum simulator
论文作者
论文摘要
成功开发量子计算机和模拟器的先决条件是对其中发生的物理过程的精确理解，这可以通过测量其产生的量子状态来实现。但是，传统的量子状态估计量表所需的资源以系统大小为指数，这突出了对替代方法的需求。在这里，我们证明了一种有效的方法来重建显着纠缠的多量子量子状态。使用矩阵乘积状态ANSATZ的变异版本，我们使用仅在每种基础上具有1000个测量值的27个基础中获取的数据，在20 Quitapped-ion Ising型量子模拟器中执行量子状态（纯状态近似）。与基于神经网络量子状态表示的方法相比，我们观察到了卓越的状态重建质量和更快的收敛速度：限制了具有自回旋体系结构的玻璃体机器和前馈神经网络。我们的结果铺平了通过多体量子系统的淬灭动力学产生的复杂状态有效实验表征的道路。
A prerequisite to the successful development of quantum computers and simulators is precise understanding of physical processes occurring therein, which can be achieved by measuring the quantum states they produce. However, the resources required for traditional quantum-state estimation scale exponentially with the system size, highlighting the need for alternative approaches. Here we demonstrate an efficient method for reconstruction of significantly entangled multi-qubit quantum states. Using a variational version of the matrix product state ansatz, we perform the tomography (in the pure-state approximation) of quantum states produced in a 20-qubit trapped-ion Ising-type quantum simulator, using the data acquired in only 27 bases with 1000 measurements in each basis. We observe superior state reconstruction quality and faster convergence compared to the methods based on neural network quantum state representations: restricted Boltzmann machines and feedforward neural networks with autoregressive architecture. Our results pave the way towards efficient experimental characterization of complex states produced by the quench dynamics of many-body quantum systems.
