852 citations
- The University of QueenslandAU127 papers
- The University of SydneyAU41 papers
- Macquarie UniversityAU29 papers
- The University of Western AustraliaAU23 papers
- Centre for Quantum Computation and Communication TechnologyAU15 papers
- Centre National de la Recherche ScientifiqueFR14 papers
- Ludwig-Maximilians-Universität MünchenDE11 papers
- University of New MexicoUS11 papers
- Renmin University of ChinaCN10 papers
- University of OtagoNZ9 papers
- The Dodd-Walls Centre for Photonic and Quantum TechnologiesNZ8 papers
- Australian National UniversityAU7 papers
7 papers · 1 filter
Quantum Correlations in the Kerr Ising Model
Michael Kewming, Sally Shrapnel, Gerard Milburn
In this article we present a full description of the quantum Kerr Ising model---a linear optical network of parametrically pumped Kerr non-linearities. We consider the non-dissapat…
Dynamically corrected gates suppress spatio-temporal error correlations as measured by randomized benchmarking
C. L. Edmunds, C. Hempel, R. J. Harris +3
Quantum error correction provides a path to large-scale quantum computers, but is built on challenging assumptions about the characteristics of the underlying errors. In particular…
Electrodynamic improvements to the theory of magnetostatic modes in ferrimagnetic spheres and their applications to saturation magnetization measurements
Jerzy Krupka, Adam Pacewicz, Bartlomiej Salski +4
Electrodynamic theory applied to the analysis of TEn0p mode resonances in ferromagnetic spheres placed either in metallic cavities or in the free space is compared with Walker-Flet…
Propagation and imaging of mechanical waves in a highly-stressed single-mode phononic waveguide
Erick Romero, Rachpon Kalra, Nicolas P. Mauranyapin +3
We demonstrate a single-mode phononic waveguide that enables robust propagation of mechanical waves. The waveguide is a highly-stressed silicon nitride membrane that supports the p…
Quantum Markovianity as a supervised learning task
Sally Shrapnel, Fabio Costa, Gerard Milburn
Supervised learning algorithms take as input a set of labelled examples and return as output a predictive model. Such models are used to estimate labels for future, previously unse…
Statistical analysis of randomized benchmarking
Robin Harper, Ian Hincks, Chris Ferrie +2
Randomized benchmarking and variants thereof, which we collectively call RB+, are widely used to characterize the performance of quantum computers because they are simple, scalable…