49 citations · 66 across the 2 of their papers we have counts for
3 papers · 1 filter
Scalable Bayesian Hamiltonian learning
Tim J. Evans, Robin Harper, Steven T. Flammia
As the size of quantum devices continues to grow, the development of scalable methods to characterise and diagnose noise is becoming an increasingly important problem. Recent metho…
Efficient learning of quantum noise
Robin Harper, Steven T. Flammia, Joel J. Wallman
Noise is the central obstacle to building large-scale quantum computers. Quantum systems with sufficiently uncorrelated and weak noise could be used to solve computational problems…
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…