Strategies to search for two-dimensional materials with long spin qubit coherence time

Abstract

Two-dimensional (2D) materials that can host qubits with long spin coherence time (T2) have the distinct advantage of integrating easily with existing microelectronic and photonic platforms, making them attractive for designing novel quantum devices with enhanced performance. However, the relative lack of 2D materials as spin qubit hosts, as well as appropriate substrates that can help maintain long T2, necessitates a strategy to search for candidates with robust spin coherence. Here, we develop a high-throughput computational workflow to predict the nuclear spin bath-driven qubit decoherence and T2 in 2D materials and heterostructures. We initially screen 1173 2D materials and find 190 monolayers with T2 > 1 ms, higher than that of naturally-abundant diamond. We then construct 1554 lattice-commensurate heterostructures between high-T2 2D materials and select 3D substrates, and we find that T2 is generally lower in a heterostructure than in the bare 2D host material; however, low-noise substrates (such as CeO2 and CaO) can help maintain high T2. To further accelerate the material screening effort, we derive analytical models that enable rapid predictions of T2 for 2D materials and heterotructures. The models offer a simple, yet quantitative, way to determine the relative contributions to decoherence from the nuclear spin baths of the 2D host and substrate in a heterostructural system. By developing a high-throughput workflow and analytical models, we expand the genome of 2D materials and their spin coherence times for the development of spin qubit platforms.

References in corpus (11)

• Quasiparticle band structures and optical properties of strained monolayer MoS2 and WS2
• Exciton Binding Energy of Monolayer WS2
• Recent Progress of the Computational 2D Materials Database (C2DB)
• Indistinguishable photons from separated silicon-vacancy centers in diamond
• Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows
• Data-driven discovery of novel 2D materials by deep generative models
• Diamond surface engineering for molecular sensing with nitrogen-vacancy centers
• Probing many-body dynamics in a two dimensional dipolar spin ensemble
• Vibronic response of a spin-1/2 state from a carbon impurity in two-dimensional WS${}_2$
• Substrate-controlled dynamics of spin qubits in low dimensional van-der-Waals materials
• Spin coherence times of point defects in two-dimensional materials from first principles