Neural Networks Architecture Evaluation in a Quantum Computer

2017/10/01 by Adenilton José da Silva, Adenilton Jose da Silva, Rodolfo Luan F. de Oliveira +1 · 6 citations
Computer Science · #Artificial neural network #Associative property #Binary number #Computability, Logic, AI Algorithms #Content-addressable memory #Initialization #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #Time delay neural network #Types of artificial neural networks #cs.NE

paper · pdf · doi:10.1109/bracis.2017.33

openalex publication_date 2017/10/01 · arxiv created 2017/11/13 · openalex created_date 2017/12/04 · arxiv updated 2018/01/22 · openalex updated_date 2026/08/05

Abstract

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.

Citations

• The quest for a Quantum Neural Network
• Nonlinear Quantum Mechanics Implies Polynomial-Time Solution forNP-Complete and #PProblems
• 0.5 Petabyte Simulation of a 45-Qubit Quantum Circuit
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer