Measuring quasiparticle dynamics for particle impact reconstruction in a superconducting qubit chip
E. Celi, R. Linehan, P. M. Harrington, M. Li, H. D. Pinckney, K. Serniak, W. D. Oliver, J. A. Formaggio
Abstract
Quasiparticle poisoning following particle impacts poses a significant challenge to the development of fault-tolerant superconducting quantum computers, as a sudden excess of quasiparticles can simultaneously degrade the coherence of multiple qubits across large device arrays. In this work, we present a statistical analysis that models the time evolution of radiation-induced qubit energy relaxation through quasiparticle density dynamics. This study provides insight into quasiparticle loss processes by distinguishing between recombination and trapping decay channels and assessing their respective impact on qubit performance. We precisely measure quasiparticle recombination in multiple transmon qubits and uncover an unexpected dependence of qubit relaxation dynamics on deposited energy. By linking correlated relaxation events across qubits to ballistic phonon propagation, we introduce a statistical localization approach to extract the energy deposited in the substrate, which is in good agreement with Monte Carlo simulation. This work establishes the quantitative framework for using an arbitrary subset of superconducting transmon qubits in a QPU as energy-resolving witness particle detectors.
AI Impact Assessments
(3 models)Scientific Impact Assessment
1. Core Contribution
This paper establishes a quantitative framework for using superconducting transmon qubits as energy-resolving, position-sensitive particle detectors by modeling quasiparticle (QP) density dynamics following ionizing radiation impacts. The key contributions are threefold:
First, the authors develop a statistical methodology (binomial maximum likelihood fitting) to extract QP recombination rates (*r*) and linear loss timescales (τ_ss) from qubit relaxation waveforms, distinguishing between recombination and trapping decay channels. This is achieved through an "average pulse" method that separates high-energy (recombination-sensitive) and low-energy (trapping-sensitive) event populations.
Second, they discover an unexpected correlation between the linear QP loss timescale τ_ss and deposited energy E_dep — a non-trivial finding suggesting energy-dependent microscopic dynamics that existing models do not predict.
Third, they demonstrate simultaneous energy and in-plane position reconstruction of particle impacts by combining single-qubit energy measurements with a ballistic phonon propagation model, validated against Geant4 Monte Carlo simulations of a ¹³⁷Cs source. This represents the first such demonstration using superconducting qubits.
2. Methodological Rigor
The statistical framework is carefully constructed. The Bayesian approach using binomial likelihoods is well-motivated given the binary nature of qubit readout. The analysis workflow is methodical: mock-data validation precedes real data analysis, systematic uncertainties are evaluated through cut variations, and known biases (e.g., energy reconstruction bias above ~500 eV due to saturation) are explicitly characterized.
The separation of *r* and τ_ss through energy-dependent sensitivity is clever — high-energy saturated pulses constrain *r* while low-energy pulses constrain τ_ss. The measured *r* values (0.052–0.095 ns⁻¹) fall within the range of prior aluminum measurements, lending credibility.
However, there are notable limitations. The phonon absorption probability p_a = 0.1 is assumed without calibration, acknowledged as a major systematic uncertainty. The fiducial volume cut removes 53% of the chip area, and only 5 of 10 qubits are used (those with slow recovery times). The energy reconstruction bias above 500 eV is a significant limitation for the energy spectrum comparison. The agreement between data and Geant4 simulation in Fig. 4, while "remarkably good" in shape, shows normalization discrepancies attributed to uncalibrated phonon dispersion — this is a substantial caveat for claiming quantitative energy resolution.
3. Potential Impact
This work sits at a productive intersection of quantum computing and particle physics, with implications for both:
For quantum computing: Understanding QP dynamics is essential for error correction in fault-tolerant quantum processors. The authors explicitly propose using a subset of qubits as "particle-sensing" witnesses within a QPU to trigger error mitigation protocols — knowing when, where, and how severely a particle impact affects the qubit lattice. This could be integrated into error correction codes, as the authors suggest citing recent work on radiation-aware decoding strategies.
For particle physics: The demonstration that transmon qubits can serve as meV-threshold energy-resolving detectors has implications for rare-event searches (dark matter, coherent neutrino scattering). While these qubits are not optimized for detection, the framework validates the detector physics concept. Purpose-built QP-sensitive devices could leverage this methodology.
For materials science: The non-invasive measurement of *r* via stochastic particle impacts (rather than on-chip injectors) could become a characterization tool for superconducting film quality across wafer-scale fabrication.
4. Timeliness & Relevance
This paper is highly timely. Correlated errors from cosmic rays and environmental radiation have been identified as a critical bottleneck for scaling superconducting quantum processors (Vepsäläinen et al. 2020, McEwen et al. 2022, Google's work on correlated errors). As quantum processors scale toward thousands of qubits for fault tolerance, radiation-induced correlated errors become increasingly problematic. The paper addresses this directly by providing tools to characterize and eventually mitigate these events.
The dual-use perspective (quantum computing mitigation + particle sensing) is increasingly recognized in the community, as evidenced by the growing body of references from 2024-2026. This work provides the missing quantitative link between QP dynamics models and experimental observables.
5. Strengths & Limitations
Key Strengths:
Notable Weaknesses:
6. Additional Observations
The paper's statistical methodology is transferable to other superconducting qubit platforms and could be adopted by groups working on quantum error correction. The Geant4 simulation chain, while not novel per se, provides a useful validation framework. The ~10% energy resolution at 100-200 eV is competitive with other phonon-mediated detectors at similar scales, though dedicated detector designs would substantially outperform this.
The work would benefit significantly from a calibration measurement with a known source providing monoenergetic deposits at the qubit level (as noted by the authors), which would resolve the normalization discrepancy and constrain p_a.
Generated Apr 16, 2026
Comparison History (48)
Paper 2 likely has higher scientific impact due to its direct relevance to a key bottleneck in scaling superconducting quantum computers (radiation-induced quasiparticles), with immediate practical applications in fault-tolerance, device design, and in situ diagnostics. It combines quantitative modeling with multi-qubit experimental measurements and introduces a broadly usable localization/energy-reconstruction method that turns QPUs into particle detectors—potentially impacting quantum engineering, cryogenic detectors, materials, and radiation instrumentation. Paper 1 is highly novel and rigorous within quantum information theory, but its impact is more specialized and less directly actionable in near-term hardware.
Paper 1 likely has higher impact due to broadly applicable, hardware-agnostic reductions in key fault-tolerance overheads (qubits and time), directly addressing a central bottleneck for scalable quantum computing. Its contributions span stabilizer measurement, magic/state preparation yield, code/layout tradeoffs, and surface-code runtime improvements—potentially influencing multiple architectures and the design of full stacks. Paper 2 is timely and rigorous for superconducting hardware and radiation events, but is more platform-specific and narrower in scope, with applications mainly in diagnostics and mitigation rather than general fault-tolerance primitives.
Paper 2 likely has higher scientific impact because it experimentally demonstrates exponential quantum speedup on large (120–156 qubit) superconducting processors using constant-depth, hardware-aware compiled circuits—an especially timely milestone with broad relevance to quantum algorithms, compilation, and benchmarking of near-term devices. The claim of accessible exponential speedup in the NISQ regime is widely impactful across quantum computing subfields and may influence both theory and hardware roadmaps. Paper 1 is rigorous and valuable for mitigating quasiparticle poisoning and enabling particle-event reconstruction, but its impact is more specialized to superconducting-device reliability and diagnostics.
Paper 1 demonstrates exponential quantum speedup on current NISQ hardware for a canonical problem (Simon's problem) through a novel constant-depth compilation strategy. This directly addresses one of quantum computing's central goals—demonstrating practical quantum advantage—and provides a methodological blueprint applicable to other algorithms. Paper 2 makes valuable contributions to understanding quasiparticle poisoning and proposes using qubits as particle detectors, but its impact is more niche, bridging quantum computing engineering and particle physics detection. Paper 1's demonstration of scalable exponential speedup on existing hardware has broader implications for the field's trajectory.
Paper 1 addresses correlated errors from radiation, which is a critical, system-wide roadblock for fault-tolerant quantum error correction. Furthermore, it introduces a highly novel cross-disciplinary application by utilizing qubits as energy-resolving particle detectors. While Paper 2 presents an impressive technical achievement in improving specific gate fidelities, Paper 1 offers a broader fundamental impact on the long-term viability of scalable quantum computing architectures.
Paper 2 demonstrates a high-fidelity (99.92%) iSWAP gate using a novel coupler architecture at a flux sweet spot, directly addressing critical engineering challenges (pulse distortion, decoherence, residual ZZ interaction) in scalable superconducting quantum computing. This has immediate, broad applicability to quantum processor design. Paper 1 is innovative in using qubits as particle detectors and characterizing quasiparticle dynamics, but its impact is more niche—primarily relevant to radiation mitigation and a novel but secondary use case (particle detection). Paper 2's contribution to gate fidelity improvement is more central to the field's core scalability challenge.
Paper 1 presents a novel quantitative framework linking quasiparticle dynamics from particle impacts to qubit performance in superconducting quantum computers, addressing a critical challenge for fault-tolerant quantum computing. It introduces an innovative approach using qubits as energy-resolving particle detectors, with cross-disciplinary implications spanning quantum computing and particle physics. Paper 2, while demonstrating a useful advance in QKD security, is more incremental—addressing transmitter-device-independence with modest key rates at limited distances. Paper 1's broader impact across quantum computing scalability and particle detection gives it higher potential.
Paper 2 has higher potential impact due to its direct relevance to fault-tolerant quantum computing—a critical bottleneck—and its dual contribution: both diagnosing quasiparticle poisoning (a major practical problem for scaling superconducting qubits) and establishing a novel framework for using qubit arrays as particle detectors. This bridges quantum computing and particle physics, offering broad interdisciplinary impact. Paper 1 makes a valuable fundamental contribution to continuous-variable quantum foundations, but its scope and practical implications are narrower compared to Paper 2's actionable insights for quantum hardware development.
Paper 2 addresses a fundamental roadblock in fault-tolerant superconducting quantum computing (quasiparticle poisoning) while simultaneously innovating by repurposing qubits as energy-resolving particle detectors. This dual-impact advances both quantum hardware coherence and novel particle detection frameworks, providing broader interdisciplinary impact compared to Paper 1's valuable but narrower incremental security improvement in quantum key distribution.
Paper 1 addresses a critical bottleneck in scaling superconducting quantum computers: quasiparticle poisoning. Its practical framework for mitigating decoherence and using qubits as energy-resolving detectors offers immediate, high-impact applications in fault-tolerant quantum hardware development. Paper 2, while providing rigorous fundamental insights into continuous-variable systems, focuses on foundational physics and lacks the immediate, broad technological applicability of Paper 1's findings to current industry and academic hardware scaling efforts.
Paper 2 establishes fundamental theoretical results bridging numerical practice and provable guarantees for tensor network belief propagation, a widely used tool across quantum computing, condensed matter physics, and machine learning. The concept of 'algorithmic locality' is novel and broadly applicable. Paper 1, while rigorous and valuable for superconducting qubit reliability, addresses a more specialized problem (quasiparticle poisoning characterization). Paper 2's breadth of impact across quantum information theory, algorithm design, and many-body physics, combined with its foundational theoretical contributions, gives it higher potential scientific impact.
Paper 1 addresses a critical bottleneck in quantum computing—achieving early fault-tolerant quantum advantage on neutral atom platforms—with concrete architectural innovations (teleportation-based parallelization, ~3× speedup) and explicit resource estimates (11,495 atoms, ~15 hours). This directly advances the path toward practical quantum advantage, which is the central goal of the entire quantum computing field. Paper 2 is rigorous and novel in characterizing quasiparticle poisoning and using qubits as particle detectors, but its impact is more niche, primarily relevant to superconducting qubit resilience and an unconventional detector application. Paper 1's breadth of impact and timeliness give it the edge.
Paper 1 addresses a critical practical challenge for fault-tolerant quantum computing—quasiparticle poisoning from particle impacts—and develops a quantitative framework with direct engineering applications. It bridges quantum computing and particle detection, enabling qubits to serve as energy-resolving detectors. This dual-use innovation, combined with its immediate relevance to the rapidly growing quantum computing industry and experimental validation against Monte Carlo simulations, gives it broader impact. Paper 2 makes a solid theoretical contribution to non-ergodic dynamics but addresses a more niche problem in quantum many-body physics with less immediate practical application.
Paper 2 addresses the barren plateau problem, one of the most fundamental challenges in variational quantum algorithms, by introducing a novel theoretical framework connecting quantum sparsity, topological entanglement entropy, and the edge of chaos. It derives a quantum Nyquist-Shannon sampling theorem and provides both theoretical insights and practical tools applicable broadly across quantum computing. Paper 1, while rigorous and valuable for understanding quasiparticle poisoning in superconducting qubits, addresses a more specialized problem with narrower impact. Paper 2's conceptual breadth—spanning quantum information theory, complexity, and practical VQA design—gives it higher cross-field impact potential.
Paper 2 likely has higher scientific impact because it addresses a broadly limiting, device-level bottleneck (radiation/particle impacts and quasiparticle poisoning) with immediate relevance to scaling superconducting QPUs. It provides experimentally grounded modeling, separates physical decay channels, discovers an unexpected energy dependence, and introduces a practical localization/energy-reconstruction method using existing qubits—opening applications in hardware diagnostics, shielding/packaging design, and real-time event mitigation across platforms. Paper 1 is novel and rigorous for BB-code peeling theory and latency, but its impact is narrower to a specific code family/decoder regime.
Paper 1 addresses a critical bottleneck in near-term quantum computing by bridging quantum error mitigation and error correction. Its method significantly reduces runtime costs and variance, offering a highly practical and broadly applicable framework for the pre-fault-tolerant regime. While Paper 2 provides valuable hardware-specific insights and a novel particle detection application, Paper 1's algorithmic advancements have a more immediate and widespread potential impact on improving computational capabilities across various quantum platforms.
Paper 2 likely has higher impact due to direct relevance to scaling fault-tolerant superconducting quantum processors: it quantifies quasiparticle dynamics under radiation, separates recombination vs trapping channels, and introduces a practical statistical method to localize particle impacts and estimate deposited energy using existing qubits. This is timely for large QPU reliability and has clear real-world applications (mitigation, diagnostics, detector functionality) across quantum computing, superconducting device physics, and radiation effects. Paper 1 is novel in topological/giant-atom waveguide QED control, but is more specialized and further from near-term deployment.
Paper 2 demonstrates a breakthrough in quantum teleportation bandwidth, achieving 1 THz all-optical quantum teleportation—a ~10,000x improvement over existing electronic feedforward limits. This addresses a fundamental bottleneck in optical quantum computing and has transformative implications for both quantum computing clock rates and quantum communication networks. While Paper 1 makes valuable contributions to understanding quasiparticle poisoning in superconducting qubits, Paper 2's demonstration of a new paradigm for ultrafast quantum information processing has broader cross-field impact, higher novelty, and more far-reaching applications in quantum internet and computing architectures.
Paper 2 is likely to have higher impact due to immediate relevance to scaling fault-tolerant superconducting quantum computers, a central and timely challenge. It combines detailed experimental measurement with statistical modeling, separates quasiparticle decay channels, reveals an unexpected energy dependence, and introduces a practical localization/energy-reconstruction method applicable across large qubit arrays. This offers broad utility (device engineering, error mitigation, radiation sensing) and a transferable quantitative framework. Paper 1 is novel and potentially high-impact for precision sensing, but appears more proposal-like and dependent on demanding experimental realization.
Paper 2 has higher likely scientific impact due to strong timeliness and broad applicability to fault-tolerant superconducting quantum computing. It combines clear experimental methodology with quantitative modeling to separate quasiparticle decay channels, reveals an unexpected energy dependence, and introduces a practical localization/energy-reconstruction framework using existing qubit arrays—an immediately actionable tool for QPU diagnosis and radiation mitigation. Paper 1 is novel in strong-field QED and could enable niche gamma-vortex sources, but real-world implementation is harder and near-term impact is narrower than improving coherence and reliability in scalable quantum processors.