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arxiv: 2604.15895 · v1 · submitted 2026-04-17 · 🪐 quant-ph · cond-mat.supr-con

Digital Predistortion for Flux Control of Tunable Superconducting Qubits

Dharun Venkateswaran , Felice Francesco Tafuri , Yuanzheng Paul Tan , Bruno Aznar Martinez , Alisa Danilenko , Likai Yang , Arnaud Carignan-Dugas , Christoph Hufnagel
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Rainer Dumke Philip Krantz Eric T. Holland
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Pith reviewed 2026-05-10 09:11 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.supr-con
keywords digital predistortionflux controlsuperconducting qubitsIIR filtersFIR filtersdistortion compensationquantum gatesflux-tunable qubits
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The pith

A digital predistortion method using IIR and FIR filters corrects step-response distortions on flux-control lines of tunable superconducting qubits to sub-percent accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a framework that measures distortions introduced by electronics, parasitics, and the cryogenic chip on flux lines and then applies a compensating filter so the delivered flux follows the intended waveform. This correction matters because two-qubit entangling gates in superconducting processors depend on precise, fast flux pulses whose fidelity drops when the actual flux deviates from the target. The approach combines an infinite-impulse-response model of the dominant dynamics with a finite-impulse-response correction and validates the result on a real flux-tunable quantum processing unit. Experiments show that after compensation the control signal tracks the ideal linear ramp with only sub-percent error. The same method is presented as enabling automated, rapid calibration of flux channels across a QPU.

Core claim

The central claim is that a digital predistortion framework built from a combination of IIR and FIR filters can characterize and compensate for the cumulative distortions on flux-control lines, so that the actual flux delivered to a tunable qubit follows the commanded waveform with only sub-percent deviation from the ideal target linear behavior.

What carries the argument

The combined IIR-plus-FIR digital predistortion filter, which first models the observed step-response distortion and then generates a pre-corrected input waveform that cancels those distortions.

If this is right

  • The method enables automated rapid calibration of flux-control channels across a superconducting QPU.
  • Compensated flux pulses improve the fidelity of two-qubit entangling gates that rely on precise flux tuning.
  • The same filter approach can be applied to other control lines that exhibit similar linear distortion.
  • Once calibrated, the predistortion can be stored and reused for repeated gate operations without re-measurement each time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique could be extended to real-time adaptive calibration during quantum algorithm execution if the filter coefficients can be updated on the fly.
  • Similar predistortion may reduce the hardware complexity of cryogenic control electronics by shifting compensation into the digital domain.
  • If the residual error after compensation remains below the threshold set by other noise sources, gate-error budgets for flux-tunable architectures could be tightened.
  • The method offers a practical route to scaling flux-tunable processors without requiring perfect analog electronics at every channel.

Load-bearing premise

The combined IIR and FIR model fully captures the dominant distortion mechanisms on the flux line without leaving uncorrected frequency-dependent effects or introducing instability.

What would settle it

A direct measurement of the on-chip flux response after the predistorted pulse is applied that shows sustained deviations larger than a few percent from the target linear ramp, or that reveals ringing or instability in the control signal.

Figures

Figures reproduced from arXiv: 2604.15895 by Alisa Danilenko, Arnaud Carignan-Dugas, Bruno Aznar Martinez, Christoph Hufnagel, Dharun Venkateswaran, Eric T. Holland, Felice Francesco Tafuri, Likai Yang, Philip Krantz, Rainer Dumke, Yuanzheng Paul Tan.

Figure 1
Figure 1. Figure 1: Concept level block diagram of the flux control signal [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Simulation results showing the distorted signal, the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: 2D-flux spectroscopy experiment results shown as a heat map [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: The voltage-to-flux response of the transmon qubit [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a digital predistortion (DPD) framework that combines infinite impulse response (IIR) and finite impulse response (FIR) filters to characterize and compensate distortions on flux-control lines of tunable superconducting qubits. Experiments on a flux-tunable QPU are reported to achieve sub-percent deviations from ideal linear step-response behavior after compensation, with the method positioned as enabling automated rapid calibration of flux channels.

Significance. If the compensation proves robust beyond step responses, the approach could reduce calibration overhead and improve two-qubit gate fidelity in flux-tunable superconducting processors. The automated filter-based method offers a practical engineering contribution to control electronics for cryogenic QPUs.

major comments (2)
  1. [Abstract / Results] Abstract and experimental results: the sub-percent deviation claim is presented without details on the measurement setup, filter fitting procedure, error bars, or number of devices tested. This information is required to assess whether the reported performance is reproducible and holds under the stated conditions.
  2. [Experiments] Experiments section: the validation is limited to step-response characterization and correction. No data or analysis is provided on actual flux pulses used for entangling gates (which contain broader frequency content) or on independent qubit-based verification such as Ramsey fringes or cross-resonance calibration to confirm that residual distortions do not affect gate fidelity.
minor comments (2)
  1. [Methods] Clarify the exact procedure and any regularization used to extract the IIR and FIR coefficients from the measured step responses; include the model equations and fitting metric.
  2. [Methods] Specify the bandwidth and sampling rate of the control electronics and cryogenic line to allow assessment of whether unmodeled high-frequency components could remain after predistortion.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment point by point below, indicating revisions made to the manuscript where appropriate.

read point-by-point responses

  1. Referee: [Abstract / Results] Abstract and experimental results: the sub-percent deviation claim is presented without details on the measurement setup, filter fitting procedure, error bars, or number of devices tested. This information is required to assess whether the reported performance is reproducible and holds under the stated conditions.

    Authors: We agree that these details are necessary to support the reproducibility of the sub-percent deviation results. In the revised manuscript we have expanded the Experiments section with a full description of the measurement setup (including the arbitrary waveform generator, cryogenic wiring, and readout chain), the least-squares procedure used to fit the combined IIR and FIR filter coefficients to the measured step response, error bars on all deviation plots (obtained from repeated measurements), and explicit statement that the data were acquired on one flux-tunable QPU across multiple control channels. These additions appear in the updated Abstract, Experiments, and Results sections. revision: yes

  2. Referee: [Experiments] Experiments section: the validation is limited to step-response characterization and correction. No data or analysis is provided on actual flux pulses used for entangling gates (which contain broader frequency content) or on independent qubit-based verification such as Ramsey fringes or cross-resonance calibration to confirm that residual distortions do not affect gate fidelity.

    Authors: We acknowledge that the experimental validation presented is restricted to step-response correction. Step responses were chosen because they directly expose the dominant low-frequency settling distortions that the IIR filter is designed to compensate, while the FIR component addresses higher-frequency transients relevant to pulse edges. In the revised manuscript we have added a frequency-domain analysis of the combined filter response and a discussion of how the achieved sub-percent time-domain accuracy is expected to translate to typical flux-pulse bandwidths. However, we do not include new experimental data on full entangling-gate waveforms or qubit-based fidelity metrics, as those measurements lie outside the scope of the present work, which focuses on the automated calibration method itself. revision: partial

standing simulated objections not resolved
  • We cannot supply experimental results on actual flux pulses for entangling gates or independent qubit-based verifications (Ramsey fringes, cross-resonance calibration) without performing additional experiments that are beyond the scope of this manuscript.

Circularity Check

0 steps flagged

No load-bearing circularity; compensation derived from measured responses without self-referential reduction

full rationale

The paper's central result is an experimental demonstration that an IIR+FIR predistorter, fitted to step-response data, produces a compensated waveform whose measured output deviates by sub-percent from the target linear ramp. This is a standard system-identification workflow with independent validation on the same class of input; no equation reduces the reported deviation to a fitted parameter by definition, no uniqueness theorem is invoked via self-citation, and no ansatz is smuggled in. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The approach rests on the standard assumption that linear time-invariant filter models suffice to describe the dominant distortions; no new physical entities or ad-hoc axioms are introduced beyond conventional digital signal processing.

free parameters (1)
  • IIR and FIR filter coefficients
    Coefficients are determined from measured step responses and therefore constitute fitted parameters whose values are not derived from first principles.

pith-pipeline@v0.9.0 · 5484 in / 1134 out tokens · 28484 ms · 2026-05-10T09:11:00.740050+00:00 · methodology

discussion (0)

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Reference graph

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