Neuro-inspired quantum associative memory using adiabatic hamiltonian evolution

Yoshihiro Osakabe, Shigeo Sato, Hisanao Akima, Masao Sakuraba, Mitsunaga Kinjo

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

It is widely believed that the real parallel computation achieved by quantum computers has an enormous computing potential. In order to expand its applicable field, we have investigated the fusion of quantum and neural computations. As a first step of implementing learning function on quantum computers, we have proposed a novel quantum associative memory (QuAM) by considering an analogy between neural associative network and qubit network. The memorizing procedure of the QuAM is realized with a Hamiltonian derived from qubit-qubit interactions, and the retrieving procedure is based on the adiabatic Hamiltonian evolution. The memory capacity of the QuAM has been nominally estimated as 2N-1 where N is a number of qubits, but its retrieve property has not been discussed in our previous study. This paper proposes a retrieving process for the QuAM and evaluates its performance in detail. The results indicate that the average of the retrieving probability is over 50% even when the qubit network memorizes 2N-1 patterns and thus the QuAM is successfully implemented.

本文言語English
ホスト出版物のタイトル2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ803-807
ページ数5
ISBN(電子版)9781509061815
DOI
出版ステータスPublished - 2017 6 30
イベント2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
継続期間: 2017 5 142017 5 19

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2017-May

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
国/地域United States
CityAnchorage
Period17/5/1417/5/19

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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