Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.

In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.

Owing to the significant progress in manufacturing desktop quantum computers, the quest to achieve efficient quantum random access memory (QRAM) became inevitable. In this paper, we propose a novel efficient random access memory for quantum computers. The proposed QRAM has a fixed structure and can be used efficiently to store both known and unknown classical/quantum data. The storage capacity of the proposed QRAM is more efficient than that of the classical RAMs and can be used to store both classical and quantum information. Furthermore, the proposed model can access an arbitrary location in O(1) compared with other state-of-the-art models.

In this paper, a novel quantum classification algorithm that is based on competitive learning is presented to classify an input pattern that results from the failures of some sensors. As long as an incomplete pattern is presented to our model, the proposed algorithm performs the competitions between the neurons by applying some unitary transformations then measures the degree of entanglement using concurrence measure to find the winner class based on the winner-take-all technique. The proposed algorithm finds the most likely winning class label in between two binary competitive classes for an incomplete pattern presented to the proposed model. Because larger scale quantum computers are still in the lab, we studied the proposed algorithm on a case study.