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arXiv 2303.05097quant-ph
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连续变量量子态的高效学习

Efficient Learning of Continuous-Variable Quantum States

Ya-Dong Wu, Yan Zhu, Giulio Chiribella, Nana Liu

备注：
12 pages, 4 figures. Comments are welcome
发表信息：
Phys. Rev. Research 6, 033280, 2024
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AI总结：

针对多模连续变量量子态完整表征实验量随模数指数增长的问题，提出估计给定点集特征函数的方法，反射对称态仅需O(log M)份副本，正特征函数态可用分束器和零差测量实现。

摘要：

连续变量量子态的表征对量子通信、传感、模拟和计算领域的应用至关重要。然而，多模量子态的完整表征所需的实验数量会随模数量呈指数增长。本文提出一种替代方法，其目标并非重构完整量子态，而是估计其在给定点集上的特征函数。对于具有反射对称性的多模态，我们证明仅需O(log M)份量子态副本即可估计M个点处的特征函数，且该复杂度与模数量无关。当特征函数已知为正时（如压缩真空态的情况），可通过仅使用分束器和零差测量的实验友好型装置完成估计。

英文摘要：

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at M points can be estimated using only O(log M ) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements.

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。

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