High speed flux sampling for tunable superconducting qubits with an embedded cryogenic transducer
B. Foxen, J.Y. Mutus, E. Lucero, E. Jeffrey, D. Sank, R. Barends, K., Arya, B. Burkett, Yu Chen, Zijun Chen, B. Chiaro, A. Dunsworth, A. Fowler, C., Gidney, M. Giustina, R. Graff, T. Huang, J. Kelly, P. Klimov, A. Megrant, O., Naaman, M. Neeley, C. Neill, C. Quintana, P. Roushan

TL;DR
This paper introduces a high-speed, on-chip flux measurement technique using a cryogenic transducer, enabling detailed characterization of superconducting qubit packages and their flux response dynamics.
Contribution
The authors develop a capacitively shunted SQUID-based flux transducer with GHz bandwidth for rapid flux measurement in superconducting qubits, demonstrating its application in package qualification.
Findings
Transducer bandwidth of 2.6 GHz enables fast flux measurements.
Step response varies significantly with material choice, affecting qubit performance.
Copper plated aluminum PCBs show response consistent with high-fidelity qubit packaging.
Abstract
We develop a high speed on-chip flux measurement using a capacitively shunted SQUID as an embedded cryogenic transducer and apply this technique to the qualification of a near-term scalable printed circuit board (PCB) package for frequency tunable superconducting qubits. The transducer is a flux tunable LC resonator where applied flux changes the resonant frequency. We apply a microwave tone to probe this frequency and use a time-domain homodyne measurement to extract the reflected phase as a function of flux applied to the SQUID. The transducer response bandwidth is 2.6 GHz with a maximum gain of allowing us to study the settling amplitude to better than 0.1%. We use this technique to characterize on-chip bias line routing and a variety of PCB based packages and demonstrate that step response settling can vary by orders of magnitude in both settling time and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
