Grant, Edward;
(2024)
Advances in Quantum Machine Learning.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
This dissertation thesis comprises three main contributions to the field of Quantum Machine Learning. The first is a variational machine learning method for classification and regression that employs a hierarchical structure based on tensor networks that can be trained to make predictions based on both quantum and classical data. The second is a method for initializing parameterized quantum circuits to avoid the problem of barren plateaus - a property of trainable quantum circuits where if the parameters of the circuit are initialized at random they can be effectively impossible to optimize because of gradients that become exponentially small in the number of qubits. The third contribution is a method for modelling quantum systems embedded in an unknown environment that can cause non-Markovian effects.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Advances in Quantum Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10202677 |
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