Characterizing Materials for Quantum Computing
Yaniv Rosen | 20-ERD-010
Material sources of loss and noise plague quantum computing devices and prevent them from achieving their full potential. Most of the coherence improvements in the last decade have been from engineering different geometries and reducing the lossy materials. However, these techniques are limited due to a lack of understanding of the origin of the noise. In this work we treated the noise as a fundamental physics problem. We started with the initial assumptions made by the community and worked through several experiments and simulations to show where the model breaks down, where it must be expanded, and how we can derive it with ab initio techniques. We experimentally showed that, instead of a single universal defect type, there are at least two classes of defects that plague materials at low temperatures. Our simulations reproduce experimental effects and show that up to 150 individual defects are significantly affecting the relaxation rate of qubits and must all be treated fully quantum mechanically. Additionally, we used ab initio techniques to discover material origins of the defects in amorphous materials, finding several that are consistent with experimental results in the literature.
In this research, we also developed methods to mitigate the noise in devices. Our simulation methodology allows us to understand how different geometries will respond to material choices and to test materials before using them in time-intensive experiments. Finally, in addition to the scientific achievements, we initiated collaborations with institutions around the world and used the research projects to train new PIs in quantum information. To further develop the quantum workforce, this project trained students on simulation and experimental methods and initiated a workshop that gave access to students around the San Francisco Bay Area to LLNL's quantum testbed.
Quantum computing is a fledgling technology that promises to revolutionize computing and quantum simulation. Just as in the case of high-performance computing, LLNL must remain competitive in quantum computing to be able to nimbly pivot to using it when the field demonstrates quantum advantage, and to provide expertise to national security missions.
In addition to training the workforce and DEI efforts in quantum computing, the research in this ER created new techniques to understand the materials sources of noise and decoherence in qubits with applications to various research efforts, including those supported by Office of Science/Basic Energy Sciences and High Energy Physics as well as exploring solutions to emerging security challenges. Furthermore, this ER developed simulation methods, devices, and infrastructure that will improve our ability to provide quantum computers for LLNL and collaborators.
Publications, Presentations, and Patents
Yu, Liuqi, et al., 2022. "Experimentally Revealing Anomalously Large Dipoles in the Dielectric of a Quantum Circuit." Scientific Reports 12.
Ray, K. GG., et al., 2022. "Ab Initio Computational Searches for TLS in Amorphous Dielectrics." APS March Meeting 2022, abstract id.Y40.008.
Rasen, Y., 2022. "QIS Materials Research for Superconducting Devices." Invited presentation, LBNL Networking Event Information, 2022.
Cho, Yujin, 2022. "How do the defects interact with a qubit?" Lawrence Livermore National Laboratory CMS WiP Seminar, 15 March 2022.