Optimal purification of a spin ensemble by quantum-algorithmic feedback
Abstract
Purifying a high-temperature ensemble of quantum particles towards a known state is a key requirement to exploit quantum many-body effects. An alternative to passive cooling, which brings a system to its ground state, is based on feedback to stabilise the system actively around a target state. This alternative, if realised, offers additional control capabilities for the design of quantum states. Here we present a quantum feedback algorithm capable of stabilising the collective state of an ensemble from an infinite-temperature state to the limit of single quanta. We implement this on ~50,000 nuclei in a semiconductor quantum dot, and show that the nuclear-spin fluctuations are reduced 83-fold down to 10 spin macrostates. While our algorithm can purify a single macrostate, system-specific inhomogeneities prevent reaching this limit. Our feedback algorithm further engineers classically correlated ensemble states via macrostate tuning, weighted bimodality, and latticed multistability, constituting a pre-cursor towards quantum-correlated macrostates.
Cite
@article{arxiv.2111.04624,
title = {Optimal purification of a spin ensemble by quantum-algorithmic feedback},
author = {Daniel M. Jackson and Urs Haeusler and Leon Zaporski and Jonathan H. Bodey and Noah Shofer and Edmund Clarke and Maxime Hugues and Mete Atatüre and Claire Le Gall and Dorian A. Gangloff},
journal= {arXiv preprint arXiv:2111.04624},
year = {2021}
}