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arXiv 1809.04404quant-ph
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基于凸优化的实量子信道重构

Reconstruction of the Real Quantum Channel via Convex Optimization

Xuan-Lun Huang, Jun Gao, Zhi-Qiang Jiao, Zeng-Quan Yan, Ling Ji, Xian-Min Jin

摘要:

量子过程层析成像通常用于完全表征未知的量子过程。然而,它可能导致一个非物理的过程矩阵,这将导致关于层析成像结果的信息丢失。凸优化广泛应用于机器学习,能够生成最适合原始数据的全局最优模型,同时将过程层析成像保持在合法区域中。只有正确地揭示过程的原始行为,才能更深入地探索它的性质,如它的相变和它的哈密顿量。因此,我们从我们在自由空间海水中获得的实验结果,使用凸优化重建真实的量子信道。此外,我们还提出了一个标准,状态偏差,以评估如何重建过程符合层析成像结果。我们相信量子过程层析成像和凸优化之间的交叉可能有助于我们向量子信道的机器学习迈进。

英文摘要:

Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimal model that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Thus, we reconstruct the real quantum channel using convex optimization from our experimental result obtained in free-space seawater. In addition, we also put forward a criteria, state deviation, to evaluate how well the reconstructed process fits the tomography result. We believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.

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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