Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution

• OSAKABE Yoshihiro

  Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University

• AKIMA Hisanao

  Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University

• SAKURABA Masao

  Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University

• KINJO Mitsunaga

  Department of Electrical and Electronics Engineering, University of the Ryukyus

• SATO Shigeo

  Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University

Abstract

There is increasing interest in quantum computing, because of its enormous computing potential. A small number of powerful quantum algorithms have been proposed to date; however, the development of new quantum algorithms for practical use remains essential. Parallel computing with a neural network has successfully realized certain unique functions such as learning and recognition; therefore, the introduction of certain neural computing techniques into quantum computing to enlarge the quantum computing application field is worthwhile. In this paper, a novel quantum associative memory (QuAM) is proposed, which is achieved with a quantum neural network by employing adiabatic Hamiltonian evolution. The memorization and retrieval procedures are inspired by the concept of associative memory realized with an artificial neural network. To study the detailed dynamics of our QuAM, we examine two types of Hamiltonians for pattern memorization. The first is a Hamiltonian having diagonal elements, which is known as an Ising Hamiltonian and which is similar to the cost function of a Hopfield network. The second is a Hamiltonian having non-diagonal elements, which is known as a neuro-inspired Hamiltonian and which is based on interactions between qubits. Numerical simulations indicate that the proposed methods for pattern memorization and retrieval work well with both types of Hamiltonians. Further, both Hamiltonians yield almost identical performance, although their retrieval properties differ. The QuAM exhibits new and unique features, such as a large memory capacity, which differs from a conventional neural associative memory.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E100.D (11), 2683-2689, 2017

  The Institute of Electronics, Information and Communication Engineers

Keywords

• associative memory
• quantum computing
• adiabatic Hamiltonian evolution
• neural network

Details

• CRID: 1390282679355982464
• NII Article ID: 130006191596
• DOI: 10.1587/transinf.2017edp7138
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E100.D/11/E100.D_2017EDP7138/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed