Invariance of success probability in Grover's quantum search under local noise with memory
Sheikh Parvez Mandal, Ahana Ghoshal, Chirag Srivastava, Ujjwal Sen

TL;DR
This paper investigates how certain types of local, time-correlated noise affect the success probability of Grover's quantum search algorithm, revealing invariance under specific noise models and highlighting the role of symmetry-breaking.
Contribution
It identifies 'good noises' that leave success probability unchanged and analyzes the effects of different Pauli noise types under time-correlated conditions.
Findings
Success probability remains invariant under specific noise models.
Pauli $\sigma_x$ and $\sigma_z$ noises do not affect success probability.
Parity of noisy qubits influences success probability under $\sigma_y$ noise.
Abstract
We analyze the robustness of Grover's quantum search algorithm performed by a quantum register under a possibly time-correlated noise acting locally on the qubits. We model the noise as originating from an arbitrary but fixed unitary evolution, , of some noisy qubits. The noise can occur with some probability in the interval between any pair of consecutive noiseless Grover evolutions. Although each run of the algorithm is a unitary process, the noise model leads to decoherence when all possible runs are considered. We derive a set of unitary 's, called the 'good noises,' for which the success probability of the algorithm at any given time remains unchanged with varying the non-trivial total number () of noisy qubits in the register. The result holds irrespective of the presence of any time-correlations in the noise. We show that only when is either of the Pauli matrices…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture
