Two key primitives in many quantum algorithms are the accurate estimation of observable expectation values and the acquisition of accurate shot counts. At present, these are challenging tasks as noise in quantum hardware affects gate fidelities as well as state preparation and measurement accuracy. These issues will be resolved once quantum error correction is available, but until then quantum error mitigation provides a viable alternative.
In this talk I will first describe how gate noise can be mitigated using probabilistic error cancellation. For this, we require an accurate and scalable model of the noise, which is achieved using out sparse Pauli-Lindblad noise model. I will explain how gate noise is shaped, learned, and finally cancelled. A second approach, which can be used to protect Clifford operations from errors and help obtain accurate shot counts, consists of coherent Pauli checks. These checks can detect certain errors, which can then be filtered out using post-selection. I will show how a simplified Markov model can be used to evaluate the (asymptotic) logical-error and post-selection rates and will discuss the results of practical experiments.
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