Lawrence Livermore National Laboratory

Detecting the Material Origin of μeV Energy Scale Defects

Better quantum computing requires better materials. A new sensor developed at LLNL is making that possible.

Understanding the impact of quantum material defects

Devices for quantum computing (QC) operate at single-photon energies, where low-energy interactions with the environment decrease the fidelity of their calculations. Defects at the μeV (microelectron) energy scales are now the dominant sources of decoherence and dephasing.

Unfortunately, the amorphous materials in quantum circuits undergo quantum tunneling themselves. This causes fluctuating defects that couple the quantized electromagnetic environment of qubits and sensors to an effective thermal vibrational bath, causing information loss.

While defects are a major problem in QC, advances have already been made in understanding how certain types of defects are related to quantum hardware. For example, defect densities correspond to the number of material configurations for a specific energy bandwidth. Meanwhile, defect electric dipole moments—caused by charge reconfiguration—determine the coupling to the QC device.

Building a better quantum materials sensor

Our project is to characterize a selection of quantum device materials using a quantum sensor that can extract information about μeV energy scale defects.

This quantum materials sensor is a type of LC resonator that is sensitive to resonant defects in materials placed on its surface. The sensor has regions where materials can be subjected to magnetic and low frequency electric fields, or bias fields that enable the detection of defect and material parameters.

In order to fully understand the device, it will initially be characterized with three QC-relevant materials: amorphous silicon, silicon oxide, and silicon nitride. We'll compare the information from the quantum materials sensor with standard material characterization techniques to find the material origin of defects.

We'll then use x-ray diffraction to understand materials' atomic structure, secondary ion mass spectrometry to determine impurity amounts, and ellipsometry to find dielectric properties. We'll then compare those measurements to the known quantum properties of the µeV energy scale fluctuators.

Finally, we'll use the quantum materials sensor to explore device operation using previously developed superconducting materials and to characterize a variety of materials that could be useful for future QC development.

The insight gained from this project will revolutionize superconducting quantum computing fabrication at LLNL and throughout the field, increasing both coherence times and quantum device functionality.

Related Publications


  • Group members that are part of the project: Yaniv Rosen, Alessandro Castelli, Daniel Tennant, Sean O'Kelley, Yujin Cho
  • Internal collaborators: Keith Ray, Kevin Chaves
  • Funding info: 10/2020 - 09/2023

A diagram of a biased-bridge resonator device illustrating how different electric and RF magnetic fields enable researchers to better understand quantum materials.
The biased-bridge resonator device is the basis for our experiments. This LC resonator couples to μeV states inside the materials placed on top of the device. By applying a dc bias at the bottom port, we can shift the energies of the μeV states and move new ones into bandwidth. This allows us to probe the density of states, look at energy decay times, and extract dipole moments.

 A graphic depicting a structural model of disorder.
LLNL generated structural model of disorder at an AI/AIXOY/AI interface. O atoms are red while Al atoms are colored according to their coordination to O: white (0), black (1), blue (2), brown (3), light blue (4), and green (5).