# Quantum perceptron over a field and neural network architecture selection in a quantum computer

arXiv: https://arxiv.org/abs/1602.00709 · HTML: https://mlsanity.com/paper/1602.00709/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2016.

## Built on by (most cited first, 7 of 7)

- [Quantum activation functions for quantum neural networks](https://mlsanity.com/paper/2201.03700/) (2022)
- [Quantum Neural Network for Quantum Neural Computing](https://mlsanity.com/paper/2305.08544/) (2023)
- [Scalable Quantum Neural Networks for Classification](https://mlsanity.com/paper/2208.07719/) (2022)
- [Experimental quantum advantage with quantum coupon collector](https://mlsanity.com/paper/2112.07884/) (2021)
- [A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets](https://mlsanity.com/paper/2309.14366/) (2023)
- [Regression and Classification with Single-Qubit Quantum Neural Networks](https://mlsanity.com/paper/2412.09486/) (2024)
- [Bernstein-Vazirani Networks: Quantum Machine Learning by Interference](https://mlsanity.com/paper/2608.19043/) (2026)

## Abstract (arXiv)

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.