DOE’s Magnetic Vortices Probe Hidden Flaws in Quantum Materials

Daniel Bafia of Fermi National Accelerator Laboratory received a DOE Early Career Award to repurpose magnetic vortices, a known source of energy loss, as a tool for identifying hidden defects in superconducting materials. Bafia’s project will focus on niobium and tantalum, type II superconductors where these vortices form when magnetic flux penetrates the material.

He aims to connect vortex behavior to defects within these materials, potentially unlocking new ways to improve qubit performance. “I want to identify what is presently limiting the performance of our qubits,” Bafia said, and is developing a new measurement framework to pinpoint the origins of energy loss.

Magnetic Vortices Probe Defects in Superconducting Materials

Magnetic vortices, typically detrimental to superconducting performance, are being repurposed as a diagnostic tool to identify defects within materials like niobium and tantalum. Daniel Bafia’s research, funded by a DOE Early Career Award, focuses on harnessing these vortices to map the hidden defects of superconductors, potentially revealing the origins of energy loss that currently limits qubit performance.

This approach moves beyond simply measuring overall dissipation, aiming instead to pinpoint the specific flaws responsible for hindering quantum computation. Each vortex, while contributing to some energy loss, also interacts with defects and interfaces within the material, becoming pinned in place.

By carefully measuring how these trapped vortices contribute to radio-frequency dissipation, Bafia hopes to establish a correlation between specific defect environments and measurable changes in vortex behavior. “My job is to figure out what the connection is between the defects and the vortices,” he said, emphasizing the need to understand this relationship for improving qubit stability.

The project builds upon Fermilab’s decade-long expertise in studying trapped magnetic flux within niobium superconducting radio-frequency cavities used in particle accelerators. This existing knowledge base, combined with Fermilab’s capabilities in materials and magnetic characterization, provides a strong foundation for adapting techniques to the challenges of superconducting quantum devices.

The work also aligns with the objectives of the Superconducting Quantum Materials and Systems Center, a National Quantum Information Science Research Center, suggesting a broader effort to address fundamental materials limitations in quantum computing. “Rather than treating vortices as another source of unwanted loss, my goal is to explore whether their interactions with defects and interfaces can become a new tool for characterizing superconducting materials,” Bafia explained. Establishing a clear link between vortex pinning, the underlying defect landscape, and qubit performance could offer a new method for characterizing sources of dissipation that have previously been difficult to isolate.

If Bafia can establish a relationship between vortex behavior, the underlying defect landscape, and qubit performance, the framework could provide a new way to characterize sources of dissipation that researchers have not been able to isolate directly. This innovative use of a traditionally problematic phenomenon could unlock new avenues for improving the performance and scalability of superconducting quantum devices.

If we can link vortex pinning to energy loss, it could provide a new way to understand the defects that limit materials for superconducting quantum computers.

Daniel Bafia, Associate Scientist at Fermi National Accelerator Laboratory
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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