Determination of quantum entanglement concurrence using multilayer perceptron neural networks.
| dc.contributor.advisor | Petruccione, Francesco. | |
| dc.contributor.advisor | Sinayskiy, Llya. | |
| dc.contributor.author | Govender, Lishen. | |
| dc.date.accessioned | 2018-10-23T08:19:11Z | |
| dc.date.available | 2018-10-23T08:19:11Z | |
| dc.date.created | 2017 | |
| dc.date.issued | 2017 | |
| dc.description | Master of Science in Physics. University of KwaZulu-Natal, Durban 2017. | en_US |
| dc.description.abstract | Artificial Neural Networks, inspired by biological neural networks, have seen widespread implementations across all research areas in the past few years. This partly due to recent developments in the field and mostly due to the increased accessibility of hardware and cloud computing capable of realising artificial neural network models. As the implementation of neural networks and deep learning in general becomes more ubiquitous in everyday life, we seek to leverage this powerful tool to aid in furthering research in quantum information science. Concurrence is a measure of entanglement that quantifies the "amount" of entanglement contained within both pure and mixed state entangled systems [1]. In this thesis, artificial neural networks are used to determine models that predict concurrence, particularly, models are trained on mixed state inputs and used for pure state prediction. Conversely additional models are trained on pure state inputs and used for mixed state prediction. An overview of the prediction performance is presented along with analysis of the predictions. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10413/15713 | |
| dc.language.iso | en_ZA | en_US |
| dc.subject.other | Deep learning. | en_US |
| dc.subject.other | Machine learning. | en_US |
| dc.title | Determination of quantum entanglement concurrence using multilayer perceptron neural networks. | en_US |
| dc.type | Thesis | en_US |