Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices

• URL: http://arxiv.org/abs/2201.11046v3
• Date: Thu, 15 May 2025 17:28:39 GMT
• Title: Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices
• Authors: Bo Yang, Rudy Raymond, Shumpei Uno,
• Abstract summary: We propose two efficient quantum readout error mitigation methods.<n>The proposed methods finish in $O(ns2)$ time for probability distributions of $n$ qubits and $s$ shots.<n>Using the proposed method, the mitigation of the 65-qubit GHZ state takes only a few seconds, and we witness the fidelity of the 29-qubit GHZ state exceeding 0.5.
• Score: 4.814193390931978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The readout error on near-term quantum devices is one of the dominant noise factors, which can be mitigated by classical postprocessing called quantum readout error mitigation (QREM). The standard QREM applies the inverse of noise calibration matrix to the outcome probability distribution using exponential computational resources to the number of measured qubits. This becomes infeasible for the current quantum devices with tens of qubits or more. Here we propose two efficient QREM methods finishing in $O(ns^2)$ time for probability distributions of $n$ qubits and $s$ shots, which mainly aim at mitigating sparse probability distributions such that only a few states are dominant. We compare the proposed methods with several recent QREM methods in the following three cases: expectation values of the GHZ state, its fidelities, and the estimation error of maximum likelihood amplitude estimation (MLAE) algorithm with a modified Grover iterator. The two cases of the GHZ state are on real IBM quantum devices, while the third is with numerical simulation. Using the proposed method, the mitigation of the 65-qubit GHZ state takes only a few seconds, and we witness the fidelity of the 29-qubit GHZ state exceeding 0.5. The proposed methods also succeed in reducing the estimation error in the MLAE algorithm, outperforming the results by other QREM methods in general.

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