Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning

• Main Authors: Masuyama, Naoki, Loo, Chu Kiong, Seera, Manjeevan, Kubota, Naoyuki
• Format: Article
• Published: Institute of Electrical and Electronics Engineers (IEEE) 2018
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/21322/; https://doi.org/10.1109/TNNLS.2017.2653114

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.