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Readout errors on near-term quantum computers can introduce significant error to the empirical probability distribution sampled from the output of a quantum circuit. These errors can be mitigated by classical postprocessing given the access of an experimental emph{response matrix} that describes the error associated with measurement of each computational basis state. However, the resources required to characterize a complete response matrix and to compute the corrected probability distribution scale exponentially in the number of qubits $n$. In this work, we modify standard matrix inversion techniques using two perturbative approximations with significantly reduced complexity and bounded error when the likelihood of high order bitflip events is strongly suppressed. Given a characteristic error rate $q$, our first method recovers the probability of the all-zeros bitstring $p_0$ by sampling only a small subspace of the response matrix before inverting readout error resulting in a relative speedup of $text{poly}left(2^{n} / big(begin{smallmatrix} n w end{smallmatrix}big)right)$, which we motivate using a simplified error model for which the approximation incurs only $O(q^w)$ error for some integer $w$. We then provide a generalized technique to efficiently recover full output distributions with $O(q^w)$ error in the perturbative limit. These approximate techniques for readout error correction may greatly accelerate near term quantum computing applications.
Readout errors are a significant source of noise for near term quantum computers. A variety of methods have been proposed to mitigate these errors using classical post processing. For a system with $n$ qubits, the entire readout error profile is spec
Current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation
Measurements on current quantum-hardware are subject to hardware imperfections that lead to readout-errors. These errors manifest themselves as a bias in quantum expectation values. Here, we propose a method to remove this bias from the expectation v
We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quan
Quantum computers are capable of efficiently contracting unitary tensor networks, a task that is likely to remain difficult for classical computers. For instance, networks based on matrix product states or the multi-scale entanglement renormalization