39 citations · 106 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Quantum Support Vector Machines for Continuum Suppression in B Meson Decays
Jamie Heredge, Charles Hill, Lloyd Hollenberg +1
Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that…