Quick Navigation
Topics
Trapped Ion Quantum Computing
Superconducting Qubits
Digital Predistortion for Flux Control of Tunable Superconducting Qubits
arXiv
Authors: Dharun Venkateswaran, Felice Francesco Tafuri, Yuanzheng Paul Tan, Bruno Aznar Martinez, Alisa Danilenko, Likai Yang, Arnaud Carignan-Dugas, Christoph Hufnagel, Rainer Dumke, Philip Krantz, Eric T. Holland
Year
2026
Paper ID
52471
Status
Preprint
Abstract Read
~2 min
Abstract Words
121
Citations
N/A
Abstract
Flux-tunable superconducting qubits rely on fast flux control pulses to implement two-qubit entangling quantum gates, a key building block for quantum algorithms. However, distortion effects introduced by non-ideal control electronics, parasitic components, and the cryogenic quantum chip response can all degrade the gate fidelity. We present a digital predistortion (DPD) framework for characterizing and then compensating for these distortions using a combination of infinite impulse response (IIR) and finite impulse response (FIR) filters. Experiments on a flux-tunable quantum processing unit (QPU) demonstrate a successful correction of step-response distortions on the flux-control line, with a compensated control signal showing only sub-percent deviations from the ideal target linear behavior. The demonstrated method enables automated rapid calibration of flux control channels for superconducting QPUs.
Why This Paper Matters
- This paper contributes to the Superconducting Qubits research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Flux-tunable superconducting qubits rely on fast flux control pulses to implement two-qubit entangling quantum gates, a key building block for quantum algorithms.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.