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
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.
AI Impact Assessments
(3 models)Scientific Impact Assessment
1. Core Contribution
This paper presents a digital predistortion (DPD) framework for compensating distortions in flux-control lines of tunable superconducting qubits. The approach combines infinite impulse response (IIR) and finite impulse response (FIR) filters in a two-stage correction scheme to linearize the voltage-to-flux transfer function. The method achieves sub-percent deviations from ideal linear behavior: 0.65% with IIR alone and 0.17% with the subsequent FIR stage. The framework is demonstrated experimentally on an IQM Garnet 20-qubit QPU using Keysight's quantum control system.
The core problem addressed — that non-ideal electronics, cryogenic components, and chip-level responses distort flux-control signals and degrade gate fidelity — is well-established in the superconducting qubit community. The contribution is primarily an engineering integration of known signal processing techniques (IIR/FIR filtering, least-squares optimization) applied to this specific quantum computing context, with experimental validation.
2. Methodological Rigor
The methodology follows a logical progression: simulation of individual distortion sources, characterization via 2D flux spectroscopy and Ramsey-style (Cryoscope) experiments, and then DPD compensation. However, several aspects limit the rigor assessment:
3. Potential Impact
The practical impact is moderate but real. Flux-line distortion compensation is a known calibration bottleneck in superconducting QPU operation. An automated DPD framework that can be integrated into commercial control hardware (Keysight QCS) has clear utility for:
However, the impact is constrained by the lack of demonstrated gate fidelity improvement and the strong coupling to a specific commercial platform. The technique itself (IIR/FIR predistortion) is well-established in telecommunications and RF engineering, so the novelty lies more in the application context than in the signal processing methodology.
4. Timeliness & Relevance
The work addresses a genuinely relevant problem. As superconducting QPUs scale to hundreds of qubits, automated calibration of flux-control channels becomes increasingly important. The timing is appropriate given the industry push toward larger, flux-tunable architectures. Recent related work (Hellings et al., 2025; Li et al., 2025) indicates this is an active area, and the present contribution fits within the current research landscape.
However, the paper does not advance significantly beyond existing methods. Cryoscope-based characterization [Rol et al., 2020] is already well-known, and IIR/FIR-based compensation has been previously explored in this context. The incremental advance is the specific two-stage IIR+FIR architecture and the demonstration that minimal filter taps suffice.
5. Strengths & Limitations
Strengths:
Limitations:
Overall Assessment
This is a competent applied engineering paper that demonstrates a practical solution to a known problem in superconducting qubit calibration. The work is well-executed within its scope but lacks the depth of analysis (particularly gate fidelity metrics and scalability data) needed to make a strong scientific impact. It reads more as a product application note with experimental validation than as a research contribution advancing the state of the art. The technique is sound, the results are clean, but the novelty is incremental and the impact demonstration is incomplete.
Generated Apr 20, 2026
Comparison History (42)
Paper 2 addresses a more fundamental and rapidly growing research area—mixed-state topological order and decoherence effects on quantum error-correcting codes. It introduces novel theoretical concepts (intrinsic mixed-state topological order from color codes, topological entanglement negativity as a probe) with broader implications for quantum information theory, condensed matter physics, and quantum error correction. Paper 1, while practically useful for QPU calibration, represents an incremental engineering contribution (digital predistortion) with narrower scope. Paper 2's conceptual novelty and cross-disciplinary relevance give it higher potential impact.
Paper 1 combines advanced contextual deep reinforcement learning with optimal control to address the complex challenge of calibrating high-dimensional quantum systems (qudits). This highly novel methodological approach offers significant advancements for next-generation quantum computing. In contrast, Paper 2 applies established classical signal processing techniques (DPD via IIR/FIR filters) to standard qubit architectures. While practical, Paper 1 demonstrates greater innovation and broader potential impact for scaling and robustly controlling advanced quantum hardware.
Paper 2 likely has higher scientific impact due to strong timeliness and direct applicability to improving gate fidelity in flux-tunable superconducting QPUs, a central bottleneck in near-term quantum computing. It demonstrates an experimentally validated, automatable calibration framework (DPD with IIR/FIR) that can be adopted broadly across labs and platforms using similar control stacks. Paper 1 is more theoretical and foundational for reflectometry/admittance modeling across devices, but its immediate real-world leverage and adoption potential are narrower and more specialized than scalable control-line calibration for QPUs.
Paper 1 introduces a novel digital predistortion framework addressing a critical bottleneck in quantum computing: gate fidelity. This methodological advancement has profound implications for scaling superconducting quantum processors and advancing quantum algorithms. In contrast, Paper 2 provides a valuable but primarily observational performance analysis of an existing commercial QKD device, offering less fundamental scientific innovation and broader theoretical impact compared to the foundational hardware improvements presented in Paper 1.
Paper 2 addresses a practical, high-demand problem in quantum computing—improving flux control fidelity for superconducting qubits, which is critical for scaling quantum processors. It offers a directly implementable engineering solution (digital predistortion) with demonstrated experimental validation on real quantum hardware. Paper 1 presents an interesting but more niche concept combining Casimir force measurements with machine learning for material characterization, but it remains largely theoretical and addresses a less pressing need. The quantum computing field's rapid growth and the immediate applicability of Paper 2's calibration framework give it broader and more timely impact.
Paper 2 has higher potential scientific impact because it demonstrates an experimentally validated, immediately deployable control-calibration method that directly improves superconducting qubit gate fidelity—one of the main bottlenecks to scaling near-term QPUs. Its DPD framework (IIR/FIR filtering) is practical, automatable, and broadly applicable across devices and labs, impacting hardware performance and operations. Paper 1 is novel and shows large resource-estimation gains, but is primarily a compiler/resource-optimization contribution whose real-world impact depends on adoption and on how closely estimators match hardware and full-stack constraints.
Paper 2 likely has higher scientific impact: it generalizes TEMPO tensor-network influence functionals to the most general linear coupling to a single Gaussian bosonic bath via multiple non-commuting operators, and addresses Trotter-error handling to ensure long-time convergence. This is a broadly applicable methodological advance for simulating open quantum dynamics across condensed matter, quantum optics, chemistry, and quantum information, with potential to become a standard tool. Paper 1 is valuable and timely for superconducting-qubit calibration, but is more engineering-specific and narrower in cross-field reach.
Paper 1 likely has higher scientific impact because it tackles a pressing, practical bottleneck in superconducting quantum computing—control-line distortion limiting two-qubit gate fidelity—and demonstrates an experimentally validated, automatable calibration method (DPD with IIR/FIR) on real hardware. This is timely and directly applicable to scaling QPUs, with potential adoption across labs and industry. Paper 2 is methodologically rigorous and valuable for formalization in quantum cryptography, but its near-term real-world uptake is constrained by the maturity of quantum distance-bounding deployments and narrower application scope.
Paper 1 addresses a fundamental knowledge gap in spin-strain dynamics of silicon vacancy centers in SiC, combining experimental and first-principles theoretical approaches to reveal new physics about metastable state transitions under strain. This has broad implications for quantum technology deployment in realistic environments. Paper 2, while practically useful, presents an engineering improvement (digital predistortion for flux control) that is more incremental in nature—applying known signal processing techniques to superconducting qubit calibration. Paper 1's deeper scientific insights into defect physics and broader applicability across quantum platforms give it higher long-term impact.
Paper 2 addresses a critical practical engineering challenge in superconducting quantum computing—flux control distortion compensation—with immediate real-world applicability to improving gate fidelities on actual quantum hardware. It presents experimental validation on a real QPU, demonstrating sub-percent deviations. While Paper 1 proposes a theoretically interesting interpretable quantum regression algorithm, its practical impact is more speculative given current NISQ limitations. Paper 2's automated calibration framework has broader near-term impact as it directly enables better quantum gate operations across the dominant superconducting qubit platform, benefiting the entire quantum computing ecosystem.
Paper 1 addresses an immediate, critical bottleneck in superconducting qubits, currently the most mature and widely adopted quantum computing platform. Its practical, automated digital predistortion method for improving gate fidelity offers highly relevant real-world applications for scaling up near-term quantum processors. Paper 2, while theoretically highly innovative, applies to photonic GKP states, a specialized and less mature technology, making its broader impact more distant compared to the immediate hardware improvements offered by Paper 1.
Paper 1 addresses a practical, immediate bottleneck in superconducting quantum computing—flux control distortions that degrade gate fidelity—with an experimentally validated digital predistortion framework. Its direct applicability to improving quantum processor performance gives it high near-term impact across the quantum computing community. Paper 2, while intellectually interesting in combining PQC with quantum teleportation, is largely theoretical, addresses a threat model that is not yet practically relevant (quantum adversaries attacking teleportation), and its distance constraints (~200 km) limit practical applicability. Paper 1's experimental validation and engineering utility give it broader, more immediate impact.
Paper 2 introduces a novel theoretical framework connecting quantum information theory with computational complexity, establishing fundamental separations between information-theoretic and complexity-constrained quantum correlations. This bridges quantum cryptography, complexity theory, and quantum information in a broadly impactful way. Paper 1 addresses an important but incremental engineering problem—calibrating flux control lines for superconducting qubits using digital predistortion filters. While practically useful, it represents an engineering refinement rather than a conceptual advance. Paper 2's foundational insights have broader potential to influence multiple research directions.
Paper 2 addresses a practical, pressing challenge in superconducting quantum computing—flux control distortion compensation—with demonstrated experimental results on real quantum hardware. Its digital predistortion framework enables automated calibration of flux control channels, directly impacting gate fidelity and scalability of quantum processors. Paper 1, while theoretically interesting in extending PID feedback to quantum optomechanical systems, remains largely theoretical. Paper 2's immediate applicability to the rapidly growing quantum computing industry, experimental validation, and relevance to improving quantum gate fidelity give it broader and more timely impact.
Paper 1 presents fundamental theoretical advances in quantum annealing for spin-glass optimization, demonstrating that quantum annealing can reach sub-threshold energy states faster than classical simulated annealing with a power-law exponent up to twice as large. This addresses a longstanding question about quantum advantage in optimization and has broad implications across quantum computing, statistical physics, and combinatorial optimization. Paper 2, while practically useful for superconducting qubit calibration, presents an incremental engineering contribution (applying known digital predistortion techniques to flux control lines) with narrower impact.
Paper 1 addresses a critical practical bottleneck in superconducting quantum computing—flux control distortion—with a demonstrated experimental solution enabling automated calibration of QPUs. Given the enormous investment and momentum in superconducting quantum computing, improvements to gate fidelity have broad, immediate impact across the field. Paper 2 presents a theoretical framework for low-frequency electric field sensing using Rydberg atoms, which is novel but narrower in scope (smart grid monitoring) and lacks experimental validation, limiting its near-term impact.
Paper 1 likely has higher impact: it addresses a concrete, immediate bottleneck in superconducting quantum processors—flux-line distortion limiting gate fidelity—and demonstrates an experimentally validated, automatable calibration/correction workflow. The approach (DPD using IIR/FIR) is methodologically rigorous and directly deployable across many flux-tunable QPUs, with clear real-world payoff in improving two-qubit gates. Paper 2 is conceptually interesting but more speculative: it trades depth for qubits, yields an approximate biased estimator, and its practical advantage over classical similarity/attention remains uncertain on near-term hardware.
Paper 2 addresses the fundamental Discrete Logarithm Problem, a cornerstone of modern public-key cryptography. By proposing a distributed algorithm that reduces quantum register size without requiring quantum communication, it directly accelerates the timeline for quantum computers to break current cryptographic standards. While Paper 1 offers practical hardware-level calibration for superconducting qubits, Paper 2 has vastly broader theoretical and real-world security implications across computer science, cryptography, and distributed quantum computing.
Paper 1 presents a novel physics-guided neural network architecture for characterizing high-dimensional quantum entanglement, achieving 128x speedup over numerical simulation with high fidelity. It introduces innovative methodological contributions (FiLM-modulated architecture, hybrid physics-informed loss with OAM conservation) applicable broadly to quantum optics and potentially other physics domains. Paper 2 addresses an important but more incremental engineering problem—flux control distortion compensation using established digital predistortion techniques (IIR/FIR filters)—with narrower scope limited to superconducting qubit calibration. Paper 1's broader methodological novelty and cross-disciplinary applicability give it higher impact potential.
Paper 1 has higher near-term scientific impact: it offers an experimentally validated, practically deployable calibration technique directly improving superconducting-qubit gate fidelity and scalability—core bottlenecks in quantum computing. The method is timely, automatable, and broadly applicable across QPUs and control stacks, likely influencing both academic and industrial efforts. Paper 2 is conceptually novel but more speculative: it relies on phenomenological nonlinear dynamics and variational approximations, with unclear empirical testability and weaker methodological grounding, limiting immediate cross-field uptake despite potential foundational interest.