Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

被引：195

作者：

Fujii, Keisuke ^{[1
,2
,3
,4
]}

Nakajima, Kohei ^{[2
,4
,5
]}

机构：

[1] Univ Tokyo, Grad Sch Engn, Photon Sci Ctr, Bunkyo Ku, 2-11-16 Yayoi, Tokyo 1138656, Japan

[2] Kyoto Univ, Hakubi Ctr Adv Res, Sakyo Ku, Yoshida Ushinomiya Cho, Kyoto 6068302, Japan

[3] Kyoto Univ, Grad Sch Sci, Dept Phys, Sakyo Ku, Kitashirakawa Oiwake Cho, Kyoto 6068502, Japan

[4] JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 3320012, Japan

[5] Univ Tokyo, Grad Sch Informat Sci & Technol, Bunkyo Ku, Tokyo 1138656, Japan

来源：

PHYSICAL REVIEW APPLIED | 2017年 / 8卷 / 02期

关键词：

CHAOS; COMPUTATION; NETWORK;

D O I：

10.1103/PhysRevApplied.8.024030

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

引用

页数：20

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