activity
20182020
most citedStatistical analysis of randomized benchmarking

49 citations · 66 across the 2 of their papers we have counts for

collaborators

6 papers

quant-ph2020

Fast Estimation of Sparse Quantum Noise

Robin Harper, Wenjun Yu, Steven T. Flammia

As quantum computers approach the fault tolerance threshold, diagnosing and characterizing the noise on large scale quantum devices is increasingly important. One of the most impor…

quant-ph201917 cited

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…

quant-ph2019

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…

quant-ph201949 cited

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…

cond-mat.mes-hall2018

Silicon qubit fidelities approaching incoherent noise limits via pulse engineering

C. H. Yang, K. W. Chan, R. Harper +12

The performance requirements for fault-tolerant quantum computing are very stringent. Qubits must be manipulated, coupled, and measured with error rates well below 1%. For semicond…

quant-ph2018

Fault-Tolerant Logical Gates in the IBM Quantum Experience

Robin Harper, Steven T. Flammia

Quantum computers will require encoding of quantum information to protect them from noise. Fault-tolerant quantum computing architectures illustrate how this might be done but have…