Neural Networks Architecture Evaluation in a Quantum Computer
This work addresses a bottleneck in neural architecture evaluation for researchers in quantum machine learning, but it appears incremental as it builds on existing quantum associative memory and learning algorithms.
The authors tackled the problem of evaluating neural network architectures without weight initialization by proposing a quantum algorithm, QNNAE, which outputs a binary result with probability proportional to network performance and has computational cost equal to training a neural network.
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.