article-journal

Linear temporal logic is a widely used method for verification of model checking and expressing the system specifications. The …

Spatial-temporal information of different water resources is essential to rationally manage, sustainably develop, and optimally utilize …

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to …

Inspired by the results of finite automata working on infinite words, we studied the quantum ω-automata with Büchi, Muller, Rabin and …

This paper introduces a variant of two-way quantum finite automata named two-way multihead quantum finite automata. A two-way quantum …

Quantum finite automata (QFA) play a crucial role in quantum information processing theory. The representation of ribonucleic acid …

During the last three decades, quantum neural computation has received a relatively high amount of attention among researchers and …

Motivated by the concept of quantum finite-state machines, we have investigated their relation with matrix product state of quantum …

We present the federated hybrid quantum–classical algorithm called a quanvolutional neural network with distributed training on different sites without exchanging data. The hybrid algorithm requires small quantum circuits to produce meaningful features for image classification tasks, which makes it ideal for near-term quantum computing. The primary goal of this work is to evaluate the potential benefits of hybrid quantum–classical and classical-quantum convolutional neural networks on non-independently and non-identically partitioned (Non-IID) and real-world data partitioned datasets among several healthcare institutions/clients.