NMR量子计算中利用压缩感知的有效量子态重构
Effective quantum state reconstruction using compressive sensing in NMR quantum computing
摘要：
随着量子系统维数的增加，重构量子系统态所需的测量次数呈指数增长，这使得高量子比特量子系统的态重构在物理量子计算实验中具有很大的挑战性。压缩传感（Compressive Sensing，CS）是一种有效的量子态重建技术，但在核磁共振（NMR）测量数据较少的情况下，CS能否重建量子态仍是未知数。本文提出了一种有效的基于CS的核磁共振量子态重建方法。与传统的基于CS的量子态重建方法不同，该方法使用的是NMR实验的实际观测数据，而不是Pauli算符的测量数据.我们在实际的NMR计算实验中实现了对量子态的测量，分别用较少的测量次数重建了2、3、4个量子比特的态。该方法易于实现，并且随着系统维数的增加，算法的执行效率更高。实验结果表明，该方法具有较高的效率和准确性，为实际核磁共振中的量子态重建提供了一种新的方法。
英文摘要：
The number of measurements required to reconstruct the states of quantum systems increases exponentially with the quantum system dimensions, which makes the state reconstruction of high-qubit quantum systems have a great challenge in physical quantum computing experiments. Compressive sensing (CS) has been verified as a effective technique in the reconstruction of quantum state, however, it is still unknown that if CS can reconstruct quantum states given the less data measured by nuclear magnetic resonance (NMR). In this paper, we propose an effective NMR quantum state reconstruction method based on CS. Different from the conventional CS-based quantum state reconstruction, our method uses the actual observation data from NMR experiments rather than the data measured by the Pauli operators. We implement measurements on quantum states in practical NMR computing experiments and reconstruct states of 2,3,4 qubits using fewer number of measurements, respectively. The proposed method is easy to implement and performs more efficiently with the increase of the system dimension size. The performance reveals both efficiency and accuracy, which provides an alternative for the quantum state reconstruction in practical NMR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
