Novel Frequency Tunable Systems for Superconducting Cavities

Alessandro Castelli | 22-FS-034

Project Overview

Ultra-low-loss microwave resonant cavities are the backbone of quantum information science (QIS) and axion/dark-matter detection (ADMX), but one of the challenges in developing 3D cavity systems has been finding a tuning mechanism that does not drastically reduce the cavity quality factor (Q) but still provides a large operating bandwidth. To alleviate this limitation, we proposed to fabricate a high-Q, 3D microwave cavity with an integrated hybrid mechanical/electrical tuning scheme to achieve both coarse and rapid fine tuning. We implemented two tuning schemes: one using a shift in kinetic inductance and one utilizing mechanical motion. We found that the kinetic-inductance method did not yield any tunability in the cavity mode, but the mechanical adjustment successfully moved the cavity resonance. The quality factors obtained were lower than originally proposed, but there is potential to improve the design on further iterations.

Mission Impact

Quantum simulations with custom Hamiltonians have been demonstrated at LLNL to calculate dynamics for a variety of applications, including high-energy-density materials and nuclear physics. This technical upgrade has the potential to increase the versatility of LLNL quantum processors, which will lead to advancements in fundamental science relevant to the quantum science and technology mission research challenges. Tunable cavities are also synergistic with the nuclear, chemical, and isotopic core competency mission, as they directly benefit the ADMX effort through an increase in the scalability of upcoming dark-matter detection systems. The successful demonstration of piezoelectric tuning for high Q cavities can lead to follow-up research opportunities in high-energy physics for dark-matter research. We are in the process of joining the Fermilab Superconducting Quantum Materials Science Center based on our experience with tuning cavities for dark-matter search, and these results can be leveraged for collaboration and future funding requests.