Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution

Yoshihiro OSAKABE; Hisanao AKIMA; Masao SAKURABA; Mitsunaga KINJO; Shigeo SATO

doi:10.1587/transinf.2017EDP7138

Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution

Yoshihiro OSAKABE, Hisanao AKIMA, Masao SAKURABA, Mitsunaga KINJO, Shigeo SATO

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.11 pp.2683-2689

Publication Date: 2017/11/01

Publicized: 2017/08/09

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7138

Type of Manuscript: PAPER

Category: Fundamentals of Information Systems

Authors

Yoshihiro OSAKABE
  Tohoku University
Hisanao AKIMA
  Tohoku University
Masao SAKURABA
  Tohoku University
Mitsunaga KINJO
  University of the Ryukyus
Shigeo SATO
  Tohoku University

Keyword

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

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Yoshihiro OSAKABE, Hisanao AKIMA, Masao SAKURABA, Mitsunaga KINJO, Shigeo SATO, "Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 11, pp. 2683-2689, November 2017, doi: 10.1587/transinf.2017EDP7138.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7138/_p

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@ARTICLE{e100-d_11_2683,
author={Yoshihiro OSAKABE, Hisanao AKIMA, Masao SAKURABA, Mitsunaga KINJO, Shigeo SATO, },
journal={IEICE TRANSACTIONS on Information},
title={Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution},
year={2017},
volume={E100-D},
number={11},
pages={2683-2689},
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.},
keywords={},
doi={10.1587/transinf.2017EDP7138},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution
T2 - IEICE TRANSACTIONS on Information
SP - 2683
EP - 2689
AU - Yoshihiro OSAKABE
AU - Hisanao AKIMA
AU - Masao SAKURABA
AU - Mitsunaga KINJO
AU - Shigeo SATO
PY - 2017
DO - 10.1587/transinf.2017EDP7138
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2017
AB - 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.
ER -