Quantum-Inspired Complex-Valued Multidirectional Associative Memory

Complex-valued neuron is one of the significant and effective innovation in artificial neural networks. It is able to deal with multi-valued pattern and oscillator models. With regard as associative memory, several types of complex-valued artificial neural associative memories have developed, and confirmed its superior abilities. Conventionally, we have developed Quantum-Inspired Mulitidirectional Associative Memory (QMAM). This model demonstrates quantum information processing in neural structures results in an exponential increase in storage capacity and can explain the extensive memory and inferencing capabilities of humans. This model is applied a fuzzy inference to weight matrix to satisfy parallelism and unitarity. In this paper, we introduce Quantum-Inspired Complex-Valued Multidirectional Associative Memory (QCMAM) to handle multi-valued information. In addition, the mathematical proofs of parallelism and unitarity for a complex-valued model are presented. The simulation experiments show that QCMAM has superior abilities comparing with conventional model.