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arxiv: 2511.08267 · v2 · submitted 2025-11-11 · 🪐 quant-ph · cond-mat.mes-hall

Enhancing Circuit Fidelity in Transmon Qubit Rings via Operation Duration Tuning under Strong Connectivity Noise

Quan Fu , Xin Wang , Rui Xiong This is my paper

Pith reviewed 2026-05-17 23:48 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.mes-hall
keywords transmon qubitsconnectivity noisegate duration tuningquantum circuit fidelityquantum error correctionmachine learning optimizationsuperconducting quantum circuitsnoisy intermediate-scale quantum
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The pith

Tuning gate operation durations boosts fidelity in transmon qubit rings even under strong connectivity noise.

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

The paper examines quantum gate operations in fully connected transmon rings and shows that adjusting how long each gate runs can raise circuit fidelity despite substantial connectivity noise. These improvements appear consistently for varying numbers of qubits and for both SWAP and general circuits. For certain initial states with symmetry or entanglement, the resulting fidelities get close to thresholds required for quantum error correction. The work also introduces a supervised machine learning model that predicts the best operation durations for new devices without needing repeated full simulations.

Core claim

Fidelity in SWAP and general circuits on transmon rings can be significantly enhanced by tuning gate operation durations, producing local maxima even in strong noise. These gains hold across different qubit counts and operation types, and for initial states with favorable symmetry or entanglement the fidelities approach quantum error correction thresholds. A supervised machine learning model accurately forecasts optimal durations for unseen devices.

What carries the argument

Tuning of gate operation durations in the presence of connectivity noise, with a supervised machine learning model to predict the optimal times.

If this is right

  • Fidelity gains appear consistently for different numbers of qubits and both SWAP and general circuits.
  • Specific initial states with symmetry or entanglement reach fidelities near quantum error correction thresholds.
  • The machine learning model allows rapid prediction of optimal durations for new devices without full simulations.
  • The approach offers a route to robust circuit design in noisy experimental settings.

Where Pith is reading between the lines

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

  • The duration-tuning principle could extend to other superconducting qubit layouts that share similar noise characteristics.
  • Hardware experiments could directly test whether the simulated fidelity peaks survive in the presence of unmodeled errors.
  • The role of initial-state symmetry suggests a way to choose starting states that naturally tolerate noise better.
  • Combining duration tuning with existing error-mitigation methods might push performance further without new hardware.

Load-bearing premise

The simulations capture the dominant connectivity noise in real transmon devices and the observed fidelity peaks remain when additional decoherence channels and control imperfections are included.

What would settle it

Execute the circuits on physical transmon hardware at the predicted optimal durations versus nearby non-optimal durations and check whether the simulated local fidelity maxima appear in the measured results.

Figures

Figures reproduced from arXiv: 2511.08267 by Quan Fu, Rui Xiong, Xin Wang.

Figure 2
Figure 2. Figure 2: FIG. 2. Schematic of a two-qubit quantum circuit subject [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: FIG. 1. Schematic of transmon-qubit devices with different [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Quantum circuit implementing SWAP operations on [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Schematic of the product state [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Fidelity of different initial states under SWAP opera [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Quantum circuits for preparing (a) [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Comparison between predicted and true values for [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Illustration of the supervised-learning workflow us [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Evolution of MSE and coefficient of determination [PITH_FULL_IMAGE:figures/full_fig_p007_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Quantum circuit illustrating the effect of noise on [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Fidelity of two-qubit states under Gaussian noise [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. Average infidelity 1 [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
read the original abstract

Superconducting transmon qubits are a promising platform for quantum computation, yet they face significant fidelity degradation due to connectivity noise, particularly in the intermediate coupling regime where noise levels are substantial. While prior works suggest that high fidelity requires operating in regimes with strongly suppressed noise, maintaining such conditions under practical experimental constraints remains challenging. To address this, we investigate quantum gate operations in fully connected transmon rings, examining both SWAP and general circuits. Our study reveals that fidelity can be significantly enhanced by tuning gate operation durations, with local maxima emerging even under strong noise conditions. These fidelity enhancements occur consistently across different qubit numbers and operation types, and for specific initial states -- particularly those with favorable symmetry or entanglement properties -- the achieved fidelities approach quantum error correction thresholds. Furthermore, we develop a supervised machine learning model that accurately predicts the optimal operation durations for new devices, enabling efficient optimization without extensive experimental simulations. These results provide a pathway toward robust quantum circuit design in noisy experimental environments.

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

3 major / 2 minor

Summary. The paper examines quantum gate operations in fully connected transmon qubit rings under strong connectivity noise. Through numerical simulations of SWAP and general circuits for varying qubit numbers, it reports that tuning gate operation durations yields significant fidelity enhancements with local maxima persisting even in high-noise regimes. For certain symmetric or entangled initial states, fidelities approach quantum error correction thresholds. A supervised machine learning model is trained on these simulation results to predict optimal durations for new devices without requiring extensive additional simulations.

Significance. If the reported fidelity peaks and their robustness hold under more complete noise models, the work would offer a practical, simulation-guided approach to circuit optimization in noisy intermediate-scale superconducting hardware, reducing reliance on ultra-low-noise operating regimes. The ML predictor could streamline device-specific tuning, though its value depends on generalization beyond the training ensemble.

major comments (3)
  1. [Simulation Setup / Noise Model] The central claim that duration tuning produces local fidelity maxima under strong noise and can approach QEC thresholds rests on simulations that include only a connectivity-noise term in the ring Hamiltonian. No section demonstrates that these peaks survive when standard transmon channels (T1/T2 relaxation, 1/f flux noise, pulse distortion, readout infidelity) are added to the Lindblad or stochastic Schrödinger equation; this omission is load-bearing for the claim of relevance to real devices.
  2. [Machine Learning Predictor] The supervised ML model is trained exclusively on data generated under the same connectivity-noise model used to identify the fidelity peaks. Consequently, predictions for 'new devices' amount to interpolation within the fitted simulation ensemble rather than an independent derivation or experimental test; this circularity directly weakens the assertion that the method provides a practical pathway for noisy hardware.
  3. [Results / Figures] The abstract and results claim consistent fidelity gains 'across different qubit numbers and operation types' and approach to QEC thresholds for specific states, yet no error bars, statistical significance tests, or details on post-hoc optimization of duration grids on the same data are reported. This leaves open whether the reported local maxima are robust or artifacts of the chosen noise-strength and duration-grid parameters.
minor comments (2)
  1. [Methods] Notation for the connectivity noise strength and the precise definition of the ring Hamiltonian should be clarified in the Methods section to allow independent reproduction.
  2. [Discussion] The manuscript would benefit from explicit comparison of the achieved fidelities against standard transmon error rates reported in recent experimental literature.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on our manuscript. We address each major comment point by point below, providing clarifications on the scope of our work and indicating where revisions will be made to improve the presentation and address concerns.

read point-by-point responses

  1. Referee: [Simulation Setup / Noise Model] The central claim that duration tuning produces local fidelity maxima under strong noise and can approach QEC thresholds rests on simulations that include only a connectivity-noise term in the ring Hamiltonian. No section demonstrates that these peaks survive when standard transmon channels (T1/T2 relaxation, 1/f flux noise, pulse distortion, readout infidelity) are added to the Lindblad or stochastic Schrödinger equation; this omission is load-bearing for the claim of relevance to real devices.

    Authors: Our study specifically isolates the effects of strong connectivity noise in the ring Hamiltonian, as this is the dominant challenge highlighted in the intermediate coupling regime for transmon rings. The results demonstrate that duration tuning yields fidelity improvements and local maxima even under this strong noise. We agree that incorporating additional channels such as T1/T2 relaxation and 1/f flux noise would provide a more complete picture for real-device relevance. In the revised manuscript, we will add an explicit limitations section discussing the simplified noise model, its implications for the observed effects, and suggestions for future extensions to full Lindblad or stochastic models. revision: partial

  2. Referee: [Machine Learning Predictor] The supervised ML model is trained exclusively on data generated under the same connectivity-noise model used to identify the fidelity peaks. Consequently, predictions for 'new devices' amount to interpolation within the fitted simulation ensemble rather than an independent derivation or experimental test; this circularity directly weakens the assertion that the method provides a practical pathway for noisy hardware.

    Authors: The ML predictor is intended to enable efficient estimation of optimal durations for new parameter sets (e.g., varying qubit numbers or noise strengths) without repeating full circuit simulations each time, within the connectivity-noise framework used throughout the paper. We will revise the relevant sections to clarify that the model performs interpolation within the trained simulation ensemble and to discuss its generalization limits explicitly. This will temper claims about applicability to arbitrary new devices while retaining the practical utility for similar hardware configurations. revision: partial

  3. Referee: [Results / Figures] The abstract and results claim consistent fidelity gains 'across different qubit numbers and operation types' and approach to QEC thresholds for specific states, yet no error bars, statistical significance tests, or details on post-hoc optimization of duration grids on the same data are reported. This leaves open whether the reported local maxima are robust or artifacts of the chosen noise-strength and duration-grid parameters.

    Authors: We agree that reporting error bars, optimization details, and statistical analysis would strengthen the results section. The local maxima were identified through systematic grid searches over gate durations for multiple qubit numbers, circuit types, and noise strengths. In the revised manuscript, we will include error bars from multiple simulation runs or noise realizations, provide explicit details on the duration-grid optimization procedure, and add statistical significance tests to confirm the robustness of the fidelity peaks and their consistency across conditions. revision: yes

Circularity Check

1 steps flagged

ML predictor of optimal durations reduces to interpolation within the same simulation ensemble

specific steps
  1. fitted input called prediction [Abstract (final paragraph)]
    "Furthermore, we develop a supervised machine learning model that accurately predicts the optimal operation durations for new devices, enabling efficient optimization without extensive experimental simulations."

    The optimal durations are first identified by scanning the same numerical simulations that demonstrate the fidelity peaks under the connectivity-noise model. Training the ML predictor on this ensemble means that 'predictions' for new devices amount to interpolation or regression within the fitted simulation data rather than an independent first-principles result.

full rationale

The core fidelity-enhancement results are obtained by direct numerical integration of the ring Hamiltonian plus connectivity-noise term, which is independent of the ML component. The supervised ML model is trained exclusively on those simulation outputs to predict optimal gate durations for new devices. This step matches the 'fitted input called prediction' pattern: the claimed predictions are statistically forced interpolations inside the fitted noise-model ensemble rather than independent derivations. No self-definitional equations, load-bearing self-citations, or uniqueness theorems appear in the provided text. The circularity is therefore partial and confined to the ML extension.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on a classical simulation of an open quantum system whose noise parameters are chosen to represent the intermediate-coupling regime; no new physical axioms are introduced beyond standard Lindblad or Kraus-map descriptions of transmon decoherence.

free parameters (2)
  • connectivity noise strength
    Noise amplitude in the intermediate regime is set by hand to values that produce substantial fidelity loss; the location of the fidelity peaks depends on this choice.
  • gate duration grid
    The range and sampling of operation times scanned to locate the local maxima are chosen by the authors.
axioms (1)
  • domain assumption Standard Markovian noise model for transmon qubits
    The simulation assumes the dominant error is connectivity-induced and can be captured by a time-independent Lindblad operator.

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