Study Reveals Key Traits for Long-Lasting Qubit Coherence
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Researchers from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), led by postdoctoral researcher Michael Toriyama and Prof. Giulia Galli, have developed a high-throughput computational strategy to identify 2D materials capable of supporting long spin coherence times for qubits. Utilizing an automated framework based on the “cluster correlation expansion” method, the team calculated coherence times for over one thousand monolayers, discovering 189 materials—including WS2 and Au-oxyselenides—that potentially exceed the coherence of diamond. This data-driven approach addresses the current lack of a comprehensive roadmap for identifying ideal 2D qubit hosts and substrates, predicting coherence times in the tens of milliseconds for promising compounds.
Predicting Coherence Times in 2D Materials Researchers at the University of Chicago Pritzker School of Molecular Engineering developed a computational strategy to predict spin coherence times in 2D materials. This approach screened over one thousand monolayers and identified 189 materials potentially exceeding the coherence times of diamond. Specifically, materials like WS2 and certain Au-oxyselenides showed predicted coherence times in the tens of milliseconds—values considered exceptional for solid-state systems. This is crucial because longer coherence times are essential for stable and useful qubits.
The team also evaluated over 1,500 combinations of 2D materials with supporting substrates, discovering substrate selection significantly impacts coherence. Substrates like ceria and calcium oxide, possessing low nuclear-spin noise, help preserve long coherence times in the 2D host material. This finding provides a clear guideline for designing high-performance 2D spin-qubit devices. The research expanded to nearly 5,000 materials using analytical models, identifying over 500 new candidates with long predicted coherence times. This work utilizes a “cluster correlation expansion” method and data-driven modeling to accelerate the discovery of qubit hosts. The researchers also developed analytical formulas to quickly estimate coherence times without extensive simulations. This allows for a broader search across materials databases. Ultimately, the study suggests the potential for discovering useful 2D quantum materials is far greater than previously known, and that AI-inspired models could further optimize material design for quantum coherence. Impact of Substrates on Qubit Coherence Substrates play a critical role in qubit coherence, potentially degrading performance unless carefully chosen. Researchers found that evaluating over 1,500 2D material-substrate combinations revealed significant impact from the substrate itself. Specifically, substrates like ceria and calcium oxide, possessing intrinsically low nuclear-spin noise, help preserve the long spin coherence times of the 2D host material. This highlights the importance of selecting both a quiet 2D material and a quiet substrate for optimal device performance. The University of Chicago Pritzker School of Molecular Engineering team developed a computational strategy to predict qubit coherence times across thousands of 2D materials interfaced with substrates. Their calculations revealed 189 monolayers capable of supporting coherence times longer than diamond, a popular qubit host. These predictions considered the impact of the substrate, emphasizing its effect on maintaining the qubit’s delicate quantum state and extending the time it can reliably store information. To accelerate material discovery, the team also developed analytical models estimating coherence times without extensive simulations. This allowed expansion of the search to nearly 5,000 additional 2D materials, identifying over 500 new candidates with potentially long coherence times. These models consider structure-based formulas, allowing for fast estimates of coherence and supporting a data-driven approach to identifying high-performance 2D spin-qubit devices. With only a few 2D materials explored so far as qubit hosts, the field has lacked a comprehensive roadmap to identify new candidates, especially since 2D materials must be placed on a supporting substrate in realistic devices.
Michael Toriyama High-Throughput Computational Strategy for Material Discovery Researchers from the University of Chicago Pritzker School of Molecular Engineering developed a high-throughput computational strategy to accelerate the discovery of 2D materials suitable for quantum information storage. This new, data-driven approach involved calculating spin coherence times for over one thousand monolayers, identifying 189 materials potentially exceeding the coherence of diamond. Compounds like WS2 and certain Au-oxyselenides showed predicted coherence times reaching tens of milliseconds – exceptional for solid-state systems – due to their low nuclear spin and naturally occurring spin-free isotopes.
The team’s strategy extended beyond just the 2D materials themselves, evaluating over 1,500 combinations with supporting substrates. This revealed substrates significantly impact coherence, necessitating careful selection; materials like ceria and calcium oxide, with low nuclear-spin noise, help preserve long coherence times. To facilitate large-scale screening, analytical models inspired by prior work on 3D materials were developed, allowing fast coherence time estimates without intensive simulations, expanding the search to nearly 5,000 additional materials. This computational work identified over 500 new candidates with long predicted coherence times, demonstrating a far richer landscape of potential 2D quantum materials than previously known. The researchers suggest that future work could utilize AI-inspired generative models to design entirely new materials optimized for quantum coherence. This data-driven strategy, according to senior author Giulia Galli, will be essential as quantum technologies transition from lab research to practical devices. Source: https://pme.uchicago.edu/news/expanding-search-quantum-ready-2d-materials Tags: