Learning rule for a quantum neural network inspired by Hebbian learning

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

Research output: Contribution to journalArticlepeer-review

Abstract

Utilizing the enormous potential of quantum computers requires new and practical quantum algorithms. Motivated by the success of machine learning, we investigate the fusion of neural and quantum computing, and propose a learning method for a quantum neural network inspired by the Hebb rule. Based on an analogy between neuron-neuron interactions and qubit-qubit interactions, the proposed quantum learning rule successfully changes the coupling strengths between qubits according to training data. To evaluate the effectiveness and practical use of the method, we apply it to the memorization process of a neuro-inspired quantum associative memory model. Our numerical simulation results indicate that the proposed quantum versions of the Hebb and anti-Hebb rules improve the learning performance. Furthermore, we confirm that the probability of retrieving a target pattern from multiple learned patterns is sufficiently high.

Original languageEnglish
Pages (from-to)237-245
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE104D
Issue number2
DOIs
Publication statusPublished - 2021 Feb 1

Keywords

  • Adiabatic quantum computation
  • Boltzmann machine
  • Hebb rule
  • Learning
  • Quantum neural network

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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