109 citations · 421 across the 13 of their papers we have counts for
13 papers
Efficient State Preparation for Quantum Machine Learning
Chris Nakhl, Maxwell West, Muhammad Usman
One of the key considerations in the development of Quantum Machine Learning (QML) protocols is the encoding of classical data onto a quantum device. In this chapter we introduce t…
A graph-theoretic approach to chaos and complexity in quantum systems
Maxwell West, Neil Dowling, Angus Southwell +4
There has recently been considerable interest in studying quantum systems via dynamical Lie algebras (DLAs) -- Lie algebras generated by the terms which appear in the Hamiltonian o…
Stabilizer Tensor Networks with Magic State Injection
Azar C. Nakhl, Ben Harper, Maxwell West +4
This work augments the recently introduced Stabilizer Tensor Network (STN) protocol with magic state injection, reporting a new framework with significantly enhanced ability to sim…
Non-Unitary Quantum Machine Learning
Jamie Heredge, Maxwell West, Lloyd Hollenberg +1
We introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the Linear Combination of Unitaries (L…
Adversarial Robustness Guarantees for Quantum Classifiers
Neil Dowling, Maxwell T. West, Angus Southwell +4
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with the…
Provably Trainable Rotationally Equivariant Quantum Machine Learning
Maxwell T. West, Jamie Heredge, Martin Sevior +1
Exploiting the power of quantum computation to realise superior machine learning algorithmshas been a major research focus of recent years, but the prospects of quantum machine lea…
Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
Maxwell T. West, Azar C. Nakhl, Jamie Heredge +4
Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications re…
Towards quantum enhanced adversarial robustness in machine learning
Maxwell T. West, Shu-Lok Tsang, Jia S. Low +5
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input…
Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging
Maxwell T. West, Martin Sevior, Muhammad Usman
The recent physical realisation of quantum computers with dozens to hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabiliti…
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
Shu Lok Tsang, Maxwell T. West, Sarah M. Erfani +1
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and ide…
Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
Maxwell T. West, Martin Sevior, Muhammad Usman
Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding…
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Maxwell T. West, Sarah M. Erfani, Christopher Leckie +3
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophisticati…
Influence of sample momentum space features on scanning tunnelling microscope measurements
Maxwell T. West, Muhammad Usman
Theoretical understanding of scanning tunnelling microscope (STM) measurements involve electronic structure details of the STM tip and the sample being measured. Conventionally, th…