Extending qubit coherence by adaptive quantum environment learning

TL;DR

This paper introduces an adaptive quantum environment learning protocol that uses measurement back-action to extend qubit coherence by narrowing environmental state distributions, demonstrated through simulations with NV centers.

Contribution

It presents a novel adaptive measurement protocol that leverages quantum measurement back-action to improve qubit coherence by learning and controlling the environment.

Findings

Significant increase in qubit coherence time with adaptive protocol

Deterministic reduction of environmental state distribution

Outperforms non-adaptive strategies in simulations

Abstract

Decoherence, resulting from unwanted interaction between a qubit and its environment, poses a serious challenge towards the development of quantum technologies. Recently, researchers have started analysing how real-time Hamiltonian learning approaches, based on estimating the qubit state faster than the environmental fluctuations, can be used to counteract decoherence. In this work, we investigate how the back-action of the quantum measurements used in the learning process can be harnessed to extend qubit coherence. We propose an adaptive protocol that, by learning the qubit environment, narrows down the distribution of possible environment states. While the outcomes of quantum measurements are random, we show that real-time adaptation of measurement settings (based on previous outcomes) allows a deterministic decrease of the width of the bath distribution, and hence an increase of the qubit coherence. We numerically simulate the performance of the protocol for the electronic spin of a nitrogen-vacancy centre in diamond subject to a dilute bath of $^{13}$C nuclear spin, finding a considerable improvement over the performance of non-adaptive strategies.