Maxwell T. West
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View article: A graph-theoretic approach to chaos and complexity in quantum systems
A graph-theoretic approach to chaos and complexity in quantum systems Open
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 of the system. However, there are some important properties t…
View article: Real classical shadows
Real classical shadows Open
Efficiently learning expectation values of a quantum state using classical shadow tomography has become a fundamental task in quantum information theory. In a classical shadows protocol, one measures a state in a chosen basis after it …
View article: Nonunitary quantum machine learning
Nonunitary quantum machine learning Open
We introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the linear combination of unitaries (LCU) method. We cover three distinct topics, beginning with …
View article: Provably Trainable Rotationally Equivariant Quantum Machine Learning
Provably Trainable Rotationally Equivariant Quantum Machine Learning Open
Exploiting the power of quantum computation to realize superior machine learning algorithms has been a major research focus of recent years, but the prospects of quantum machine learning (QML) remain dampened by considerable technical chal…
View article: Adversarial Robustness Guarantees for Quantum Classifiers
Adversarial Robustness Guarantees for Quantum Classifiers Open
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data. The prospect of near-term quantum computers …
View article: Provably Trainable Rotationally Equivariant Quantum Machine Learning
Provably Trainable Rotationally Equivariant Quantum Machine Learning Open
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 learning (QML) remain dampened by considerable technical chall…
View article: Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning Open
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 remains impeded by challenges. Amongst those, a key barrier i…
View article: Reflection equivariant quantum neural networks for enhanced image classification
Reflection equivariant quantum neural networks for enhanced image classification Open
Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of …
View article: Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging
Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging Open
The recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machin…
View article: Benchmarking adversarially robust quantum machine learning at scale
Benchmarking adversarially robust quantum machine learning at scale Open
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology, and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully desi…
View article: Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging
Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging Open
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 capabilities. One area that has received particular attention is quan…
View article: Hybrid Quantum–Classical Generative Adversarial Network for High-Resolution Image Generation
Hybrid Quantum–Classical Generative Adversarial Network for High-Resolution Image Generation Open
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems, such as classification and identification tasks. A subclass of QML methods is quantum gener…
View article: Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation Open
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems pertaining classification and identification tasks. A subclass of QML methods is quantum gen…
View article: Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification Open
Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of …
View article: Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Benchmarking Adversarially Robust Quantum Machine Learning at Scale Open
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophistication, neural networks can be easily fooled by carefully desig…