86 citations · 558 across the 51 of their papers we have counts for
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Kernel Alignment for Quantum Support Vector Machines Using Genetic Algorithms
Floyd M. Creevey, Jamie A. Heredge, Martin E. Sevior +1
The data encoding circuits used in quantum support vector machine (QSVM) kernels play a crucial role in their classification accuracy. However, manually designing these circuits po…
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…
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…
Permutation Invariant Encodings for Quantum Machine Learning with Point Cloud Data
Jamie Heredge, Charles Hill, Lloyd Hollenberg +1
Quantum Computing offers a potentially powerful new method for performing Machine Learning. However, several Quantum Machine Learning techniques have been shown to exhibit poor gen…