Implementation of a Quantum Perceptron
2022 Undergraduate Research Symposium
Winner ðŸ†: Lucas Huss (physics)
Faculty mentor: Alex Matos Abiague and Luca Candelori
Abstract
Quantum Artificial Intelligence (QAI) is an emerging field that could lead to a dramatic decrease in computational power consumption. QAI uses quantum computers which have some key advantages allowing for computational tasks not possible to do with classical computers.
The most intuitive way to create QAI is to mimic a neuron, this model is referred to as a quantum perceptron. Current quantum perceptron models require the use of fault-tolerant quantum computers. However, today's quantum computers are still noisy and prone to errors, which limits the practical implementation of previously proposed quantum perceptron models.
In this research we implement a quantum perceptron made for today's quantum computers, this is done by lowering the error threshold and improving the training procedure. The model is implemented on IBM's quantum simulator and is applied to a pattern recognition task.
Poster pitch
Poster
Rate this presentation
Lucas Huss: Implementation of a Quantum Perceptron