182 citations · 2.2k across the 60 of their papers we have counts for
60 papers
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…
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…
GASP -- A Genetic Algorithm for State Preparation
Floyd M. Creevey, Charles D. Hill, Lloyd C. L. Hollenberg
The efficient preparation of quantum states is an important step in the execution of many quantum algorithms. In the noisy intermediate-scale quantum (NISQ) computing era, this is…
Noise-robust ground state energy estimates from deep quantum circuits
Harish J. Vallury, Michael A. Jones, Gregory A. L. White +3
In the lead up to fault tolerance, the utility of quantum computing will be determined by how adequately the effects of noise can be circumvented in quantum algorithms. Hybrid quan…
Filtering crosstalk from bath non-Markovianity via spacetime classical shadows
Gregory A. L. White, Kavan Modi, Charles D. Hill
From an open system perspective non-Markovian effects due to a nearby bath or neighbouring qubits are dynamically equivalent. However, there is a conceptual distinction to account…
A kernel-based quantum random forest for improved classification
Maiyuren Srikumar, Charles D. Hill, Lloyd C. L. Hollenberg
The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation. There is therefore an imperative…