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Machine-Learning Quantum States in the NISQ Era
Annual Review of Condensed Matter Physics ( IF 14.3 ) Pub Date : 2020-03-16 , DOI: 10.1146/annurev-conmatphys-031119-050651
Giacomo Torlai 1 , Roger G. Melko 2, 3
Affiliation  

We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpretability and utility, we discuss in detail the theory of the restricted Boltzmann machine. We demonstrate its practical use for state reconstruction, starting from a classical thermal distribution of Ising spins, then moving systematically through increasingly complex pure and mixed quantum states. We review recent techniques in reconstruction of a cold atom wavefunction, intended for use on experimental noisy intermediate-scale quantum (NISQ) devices. Finally, we discuss the outlook for future experimental state reconstruction using machine learning in the NISQ era and beyond.

中文翻译:


NISQ时代的机器学习量子态

我们回顾了机器学习中生成建模技术的发展,目的是重建真实的,嘈杂的,多量子位的量子态。受其可解释性和实用性的激励,我们详细讨论了受限玻尔兹曼机的理论。我们展示了其在状态重建中的实际用途,它从伊辛自旋的经典热分布开始,然后系统地移动通过越来越复杂的纯和混合量子态。我们回顾了冷原子波函数重建的最新技术,该技术旨在用于实验性噪声中等规模的量子(NISQ)设备。最后,我们讨论了NISQ时代及以后使用机器学习进行的未来实验状态重构的前景。

更新日期:2020-04-21
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