In this paper, the estimations of mechanical property of rolled steel bar by using quantum neural network (QNN) were proposed. Based on the learning capability of neural network, the nonlinear, complex relationships among the steel bar, the billet materials and the control parameters of production could be automatically developed. Such an artificial intelligent (AI) estimator then can help the ...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that exploits quantum source's graph structure to improve learning via arbitrary neural network (QNN) ansatz. In particular, devise and optimize a self-supervised objective capture information-theoretic closeness of states in training QNN. Numerical simulations show our improves efficiency generalizatio...

Abstract The noisy intermediate-scale quantum devices enable the implementation of variational circuit (VQC) for neural networks (QNN). Although VQC-based QNN has succeeded in many machine learning tasks, representation and generalization powers VQC still require further investigation, particularly when dimensionality classical inputs is concerned. In this work, we first put forth an end-to-end...

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...

A bstract In this work, our prime objective is to study the phenomena of quantum chaos and complexity in machine learning dynamics Quantum Neural Network (QNN). Parameterized Circuits (PQCs) hybrid quantum-classical framework introduced as a universal function approximator perform optimization with Stochastic Gradient Descent (SGD). We employ statistical differential geometric approach theory Q...

Starting with the theoretical basis of quantum computing, entanglement has been explored as one of the key resources required for quantum computation, the functional dependence of the entanglement measures on spin correlation functions has been established and the role of entanglement in implementation of QNN has been emphasized. Necessary and sufficient conditions for the general two-qubit sta...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...