Scientists Tame Quantum Bits in a Widely Used Semiconductor Material
Quantum computers, leveraging the unique properties of qubits, outperform classical systems by simultaneously existing in multiple states. Focused research on silicon carbide aims to optimize qubits for scalable application, with studies revealing new methods to control and enhance their performance. This could lead to breakthroughs in large-scale quantum computing and sensor technologies.
Quantum Computing Foundations
While conventional computers use classical bits for calculations, quantum computers use quantum bits, or qubits, instead. While classical bits can have the values 0 or 1, qubits can exist in a mix of probabilities of both values at the same time. This makes quantum computing extremely powerful for problems conventional computers aren’t good at solving. To build large-scale quantum computers, researchers need to understand how to create and control materials that are suitable for industrial-scale manufacturing.
Semiconductors are very promising qubit materials. Semiconductors already make up the computer chips in cell phones, computers, medical equipment, and other applications. Certain types of atomic-scale defects, called vacancies, in the semiconductor silicon carbide (SiC) show promise as qubits. However, scientists have a limited understanding of how to generate and control these defects. By using a combination of atomic-level simulations, researchers were able to track how these vacancies form and behave.
Advancements in Quantum Materials
Quantum computing could revolutionize our ability to answer challenging questions. Existing small scale quantum computers have given a glimpse of the technology’s power. To build and deploy large-scale quantum computers, researchers need to know how to control qubits made of materials that make technical and economic sense for industry.
The research identified the stability and molecular pathways to create the desired vacancies for qubits and determine their electronic properties.
These advances will help the design and fabrication of spin-based qubits with atomic precision in semiconductor materials, ultimately accelerating the development of next-generation large-scale quantum computers and quantum sensors.
Challenges in Quantum Computing Development
The next technological revolution in quantum information science requires researchers to deploy large-scale quantum computers that ideally can operate at room temperature. The realization and control of qubits in industrially relevant materials is key to achieving this goal.
In the work reported here, researchers studied qubits built from vacancies in silicon carbide (SiC) using various theoretical methods. Until now, researchers knew little about how to control and engineer the selective formation process for the vacancies. The involved barrier energies for vacancy migration and combination pose the most difficult challenges for theory and simulations.
Breakthroughs in Quantum Computing Research
In this study, a combination of state-of-the-art materials simulations and neural-network-based sampling technique led researchers at the Department of Energy’s (DOE) Midwest Center for Computational Materials (MICCoM) to discover the atomistic generation mechanism of qubits from spin defects in a wide-bandgap semiconductor.
The team showed the generation mechanism of qubits in SiC, a promising semiconductor with long qubit coherence times and all-optical spin initialization and read-out capabilities.
MICCoM is one of the DOE Computational Materials Sciences centers across the country that develops open-source, advanced software tools to help the scientific community model, simulate, and predict the fundamental properties and behavior of functional materials. The researchers involved in this study are from Argonne National Laboratory and the University of Chicago.
Reference: “Stability and molecular pathways to the formation of spin defects in silicon carbide” by Elizabeth M. Y. Lee, Alvin Yu, Juan J. de Pablo and Giulia Galli, 3 November 2021, Nature Communications.
DOI: 10.1038/s41467-021-26419-0
This work was supported by the Department of Energy (DOE) Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division and is part of the Basic Energy Sciences Computational Materials Sciences Program in Theoretical Condensed Matter Physics. The computationally demanding simulations used several high-performance computing resources: Bebop in Argonne National Laboratory’s Laboratory Computing Resource Center; the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility; and the University of Chicago’s Research Computing Center. The team was awarded access to ALCF computing resources through DOE’s Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. Additional support was provided by NIH.
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