no code implementations • 22 Jun 2023 • Maxwell T. West, Shu-Lok Tsang, Jia S. Low, Charles D. Hill, Christopher Leckie, Lloyd C. L. Hollenberg, Sarah M. Erfani, Muhammad Usman
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge.
no code implementations • 22 Feb 2023 • Floyd M. Creevey, Charles D. Hill, Lloyd C. L. Hollenberg
Results achieved by GASP outperform Qiskit's exact general circuit synthesis method on a variety of states such as Gaussian states and W-states, and consistently show the method reduces the number of gates required for the quantum circuits to generate these quantum states to the required accuracy.
1 code implementation • 5 Oct 2022 • 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.