Greedy versus Map-based Optimized Adaptive Algorithms for random-telegraph-noise mitigation by spectator qubits

• URL: http://arxiv.org/abs/2205.12566v3
• Date: Wed, 21 Dec 2022 04:58:20 GMT
• Title: Greedy versus Map-based Optimized Adaptive Algorithms for random-telegraph-noise mitigation by spectator qubits
• Authors: Behnam Tonekaboni, Areeya Chantasri, Hongting Song, Yanan Liu, Howard M. Wiseman
• Abstract summary: In a scenario where data-storage qubits are kept in isolation as far as possible, noise mitigation can still be done using additional noise probes. We construct a theoretical model assuming projective measurements on the qubits, and derive the performance of different measurement and control strategies. We show, analytically and numerically, that MOAAAR outperforms the Greedy algorithm, especially in the regime of high noise sensitivity of SQ.
• Score: 6.305016513788048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a scenario where data-storage qubits are kept in isolation as far as possible, with minimal measurements and controls, noise mitigation can still be done using additional noise probes, with corrections applied only when needed. Motivated by the case of solid-state qubits, we consider dephasing noise arising from a two-state fluctuator, described by random telegraph process, and a noise probe which is also a qubit, a so-called spectator qubit (SQ). We construct the theoretical model assuming projective measurements on the SQ, and derive the performance of different measurement and control strategies in the regime where the noise mitigation works well. We start with the Greedy algorithm; that is, the strategy that always maximizes the data qubit coherence in the immediate future. We show numerically that this algorithm works very well, and find that its adaptive strategy can be well approximated by a simpler algorithm with just a few parameters. Based on this, and an analytical construction using Bayesian maps, we design a one-parameter ($\Theta$) family of algorithms. In the asymptotic regime of high noise-sensitivity of the SQ, we show analytically that this $\Theta$-family of algorithms reduces the data qubit decoherence rate by a divisor scaling as the square of this sensitivity. Setting $\Theta$ equal to its optimal value, $\Theta^\star$, yields the Map-based Optimized Adaptive Algorithm for Asymptotic Regime (MOAAAR). We show, analytically and numerically, that MOAAAR outperforms the Greedy algorithm, especially in the regime of high noise sensitivity of SQ.

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