Engineering long-lived entanglement through dissipation in quantum hybrid solid-state platforms
Jayakrishnan M. P. Nair, Benedetta Flebus

TL;DR
This paper proposes a method to generate long-lasting spin squeezing and entanglement in solid-state qubit ensembles by leveraging quantum correlations in a squeezed solid-state bath, with potential for robust quantum technologies.
Contribution
It introduces a novel scheme for achieving steady-state spin squeezing in solid-state systems via dissipation and bath engineering, expanding quantum entanglement methods.
Findings
Ensemble can exhibit steady-state spin squeezing under specific conditions.
Quantum correlations in the bath can be transferred to the qubit array.
Feasibility demonstrated through analysis of spin defects coupled to a ferromagnetic bath.
Abstract
Spin squeezing, a form of many-body entanglement, is a crucial resource in quantum metrology and information processing. While experimentally viable protocols for generating stable spin squeezing have been proposed in quantum optics setups, there is growing interest in quantum hybrid solid-state systems as alternative platforms for both engineering and exploring many-body quantum phenomena. In this work, we propose a scheme to generate long-lived spin squeezing in an ensemble of solid-state qubits interacting with electromagnetic noise emitted by a squeezed solid-state bath. We identify the conditions under which quantum correlations within the bath can be transferred to the qubit array, driving it into an entangled state independently of its initial configuration. To assess the experimental feasibility of our approach, we analyze the dynamics of an array of solid-state spin defects…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Diamond and Carbon-based Materials Research · Neural Networks and Reservoir Computing
