Development and Demonstration of an Efficient Readout Error Mitigation Technique for use in NISQ Algorithms

• URL: http://arxiv.org/abs/2303.17741v2
• Date: Thu, 20 Apr 2023 16:35:25 GMT
• Title: Development and Demonstration of an Efficient Readout Error Mitigation Technique for use in NISQ Algorithms
• Authors: Andrew Arrasmith, Andrew Patterson, Alice Boughton, and Marco Paini
• Abstract summary: We consider the approximate state estimation of readout-mitigated expectation values on the Rigetti quantum computing hardware. We show that we can suppress the effect of correlated errors and accurately mitigate the readout errors. This development opens the way for practical uses of methods with this type of randomisation.
• Score: 2.1279211992135068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The approximate state estimation and the closely related classical shadows methods allow for the estimation of complicated observables with relatively few shots. As these methods make use of random measurements that can symmetrise the effect of readout errors, they have been shown to permit simplified approaches to readout error mitigation which require only a number of samples that scales as $\mathcal{O}(1)$ with increasing numbers of qubits. However, these techniques require executing a different circuit at each shot, adding a typically prohibitive amount of latency that prohibits their practical application. In this manuscript we consider the approximate state estimation of readout-mitigated expectation values, and how to best implement that procedure on the Rigetti quantum computing hardware. We discuss the theoretical aspects involved, providing an explicit computation of the effect of readout error on the estimated expectation values and how to mitigate that effect. Leveraging improvements to the Rigetti control systems, we then demonstrate an efficient implementation of this approach. Not only do we find that we can suppress the effect of correlated errors and accurately mitigate the readout errors, we find that we can do so quickly, collecting and processing $10^6$ samples in less than $1.5$ minutes. This development opens the way for practical uses of methods with this type of randomisation.

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