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arXiv 2007.08327quant-ph
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通过机器学习对量子计算机进行“编程”的非算法方法

A non-algorithmic approach to "programming" quantum computers via machine learning

Nathan Thompson, James Steck, Elizabeth Behrman

备注：
9 pages, 21 figures. Submitted to QCE 2020
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摘要：

实现宏观量子计算的主要障碍仍然存在：噪声、退相干和缩放的硬件问题;纠错的软件问题;以及最重要的算法构造。找到真正的量子算法是相当困难的，许多真正的量子算法，如Shor的素因子分解或相位估计，需要非常长的电路深度的任何实际应用，这需要纠错。相比之下，我们证明了机器学习可以作为一种系统的方法来构建算法，也就是说，非算法地“编程”量子计算机。量子机器学习使我们能够执行计算，而无需将算法分解为门“构建块”，从而消除了这一困难的步骤，并通过简化和减少不必要的复杂性来提高效率。此外，我们的非算法机器学习方法对噪声和去相干都具有鲁棒性，这对于在固有噪声的NISQ设备上运行是理想的，这些设备在可用于纠错的量子位数方面受到限制。我们证明了这一点，使用一个基本的非经典计算：实验估计一个未知的量子态的纠缠。结果已成功移植到IBM硬件，并使用混合强化学习方法进行训练。

英文摘要：

Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many of these genuine quantum algorithms, like Shor's prime factoring or phase estimation, require extremely long circuit depth for any practical application, which necessitates error correction. In contrast, we show that machine learning can be used as a systematic method to construct algorithms, that is, to non-algorithmically "program" quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate "building blocks", eliminating that difficult step and potentially increasing efficiency by simplifying and reducing unnecessary complexity. In addition, our non-algorithmic machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrate this using a fundamentally non-classical calculation: experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a hybrid reinforcement learning method.

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