102 citations · 160 across the 5 of their papers we have counts for
5 papers
QAOA Performance in Noisy Devices: The Effect of Classical Optimizers and Ansatz Depth
Aidan Pellow-Jarman, Shane McFarthing, Ilya Sinayskiy +3
The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions f…
Hybrid Genetic Optimisation for Quantum Feature Map Design
Rowan Pellow-Jarman, Anban Pillay, Ilya Sinayskiy +1
Kernel methods are an important class of techniques in machine learning. To be effective, good feature maps are crucial for mapping non-linearly separable input data into a higher…
Classical Ensembles of Single-Qubit Quantum Variational Circuits for Classification
Shane McFarthing, Anban Pillay, Ilya Sinayskiy +1
The quantum asymptotically universal multi-feature (QAUM) encoding architecture was recently introduced and showed improved expressivity and performance in classifying pulsar stars…
Near Term Algorithms for Linear Systems of Equations
Aidan Pellow-Jarman, Ilya Sinayskiy, Anban Pillay +1
Finding solutions to systems of linear equations is a common prob\-lem in many areas of science and engineering, with much potential for a speedup on quantum devices. While the Har…
A Comparison of Various Classical Optimizers for a Variational Quantum Linear Solver
Aidan Pellow-Jarman, Ilya Sinayskiy, Anban Pillay +1
Variational Hybrid Quantum Classical Algorithms (VHQCAs) are a class of quantum algorithms intended to run on noisy intermediate-scale quantum (NISQ) devices. These algorithms empl…