Quantum perceptron over a field and neural network architecture selection in a quantum computer
This work addresses neural network architecture selection in quantum computing, offering a novel approach but is incremental as it builds on existing quantum perceptron models.
The authors tackled the problem of designing quantum neural networks by proposing a quantum perceptron over a field (QPF) to address drawbacks in previous models, and introduced a learning algorithm (SAL) that optimizes weights and architectures with linear time over training patterns, achieving polynomial-time architecture selection.
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator.