An echo state network architecture based on quantum logic gate and its optimization

Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance

Intelligent Self-Organized Robust Control Design based on Quantum/Soft Computing Technologies and Kansei Engineering

System of systems engineering technology describes the possibility of ill-defined (autonomous or hierarchically connected) dynamic control system design that includes human decision making in unpredicted (unforeseen) control situations. Kansei/Affective Engineering technology and its toolkit include qualitative description of human being emotion, instinct and intuition that are used effectively in design processes of smart/wise robotics and intelligent mechatronics. In presented report the way how these technologies can be married using new types of unconventional computational intelligence is described. System analysis of interrelations between these two important technologies is discussed. The solution of an important problem as robust intelligent control system design based on quantum knowledge base self-organization in unpredicted control situations and information risk is proposed. The background of applied unconventional computational intelligence is soft and quantum computing technologies. Applications of the developed approach in robust integrated fuzzy intelligent control systems are considered using concrete Benchmarks