Neural Networks Architecture Evaluation in a Quantum Computer

In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artificial neural networks. Unlike conventional algorithms for evaluating neural network architectures, QNNAE does not depend on initialization of weights. The proposed algorithm has a binary output and results in 0 with probability proportional to the performance of the network. And its computational cost is equal to the computational cost to train a neural network

Analisis Quantum Perceptron Untuk Memprediksi Jumlah Pengunjung Ucok Kopi Pematangsiantar Pada Masa Pandemi Covid-19

Quantum perceptron adalah merupakan metode jaringan saraf tiruan yang memadukan antara algoritma perceptron dengan komputasi quantum. Pada penelitian ini, peneliti melakukan analisis quantum perceptron untuk memprediksi jumlah pengunjung pada ucok kopi Pematangsiantar pada masa pandemi Covid-19. Dalam memprediksi jumlah pengunjung pada Ucok Kopi Pematangsiantar, peneliti menggunakan data pengunjung sebelumnya pada masa panedmi Covid-19. Variabel yang digunakan adalah 10 varibel dimulai dari x1 sampai dengan x10. Hasil dari penelitian ini adalah analisis quantum perceptron untuk memprediksi jumlah pengunjung ucok kopi Pematangsiantar

Cortico-hippocampal computational modeling using quantum neural networks to simulate classical conditioning paradigms

Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies