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arxiv: 2604.24551 · v1 · submitted 2026-04-27 · 🪐 quant-ph · cs.LG

Recognition: unknown

GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation

Daniel Krutz, Dylan Jay Van Allen, Jason Pollack, Sheeraja Rajakrishnan, Steven Szachara, Travis Desell

Authors on Pith no claims yet

Pith reviewed 2026-05-08 04:00 UTC · model grok-4.3

classification 🪐 quant-ph cs.LG
keywords quantum error mitigationadaptive quantum computingcontextual multi-armed banditsGaussian process forecastingself-organizing mapstelemetry analysisnoise mitigationlogical fidelity
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The pith

GSC-QEMit adaptively selects quantum error mitigation levels using telemetry to improve fidelity by 9 percent while lowering overhead.

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

The paper presents GSC-QEMit as a way to handle time-varying noise on near-term quantum devices by switching between light and heavy error mitigation as needed. It does this through three linked parts: clustering device telemetry into contexts, forecasting upcoming fidelity losses, and using a bandit algorithm to pick the right mitigation strength based on predicted cost and benefit. A sympathetic reader would care because fixed mitigation strategies either waste resources or fail to protect results when noise changes. If successful, this approach allows more reliable execution of quantum circuits without constant high overhead.

Core claim

GSC-QEMit is a telemetry-driven hierarchical forecast-and-bandit framework that switches between lightweight suppression and heavier intervention as noise drift evolves. It improves average logical fidelity by +9.0% relative to unmitigated execution on benchmark circuits while reducing unnecessary heavy interventions by applying them only for inferred noise spikes, with policies that transfer across workloads.

What carries the argument

The GSC-QEMit pipeline that couples a Growing Hierarchical Self-Organizing Map (GHSOM) for clustering streaming telemetry into operating contexts, an uncertainty-aware subsampled Gaussian-process forecaster for predicting short-horizon fidelity degradation, and a cost-aware contextual multi-armed bandit (CMAB) using Thompson sampling to select mitigation actions.

Load-bearing premise

The assumption that clustering telemetry produces meaningful contexts, that the forecaster reliably predicts fidelity changes, and that the bandit choices work outside the specific simulated noise patterns.

What would settle it

Observing that on a real quantum processor the adaptive selections produce no net fidelity gain or higher total runtime cost than a static mitigation policy would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.24551 by Daniel Krutz, Dylan Jay Van Allen, Jason Pollack, Sheeraja Rajakrishnan, Steven Szachara, Travis Desell.

Figure 1
Figure 1. Figure 1: GSC-QEMit implementation: An instrumented backend streams view at source ↗
Figure 2
Figure 2. Figure 2: Real-time adaptive response (case study: QFT). A representative execution trace under drifting noise. Top strip (barcode): the selected intervention level over time (NONE → MODERATE → SEVERE). Barcode shading encodes action: white/green = NONE, gray/orange = MODER￾ATE, black/purple = SEVERE. Bottom plot: logical fidelity FL (solid) alongside the drifting effective noise indicator (dotted). Key insight: dur… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-benchmark stabilization under drift. Time-series traces for the evaluation suite under a controlled, time-varying noise schedule with a deliberate mid-run noise peak. Dotted: imposed effective noise indicator peff (t). Dashed: unmitigated baseline logical fidelity. Solid: adaptive fidelity under GSC-QEMit. The reduced depth of the mid-run fidelity trough under the adaptive policy reflects active miti… view at source ↗
read the original abstract

Quantum error mitigation (QEM) is essential for extracting reliable results from near-term quantum devices, yet practical deployments must balance mitigation strength against runtime overhead under time-varying noise. We introduce \emph{GSC-QEMit}, a telemetry-driven, \textbf{context--forecast--bandit} framework for \emph{adaptive} mitigation that switches between lightweight suppression and heavier intervention as drift evolves. GSC-QEMit composes three coupled modules: (G) a Growing Hierarchical Self-Organizing Map (GHSOM) that clusters streaming telemetry into operating contexts; (S) an uncertainty-aware subsampled Gaussian-process forecaster that predicts short-horizon fidelity degradation; and (C) a cost-aware contextual multi-armed bandit (CMAB) that selects mitigation actions via Thompson sampling with explicit intervention cost. We evaluate GSC-QEMit on benchmark circuit families (GHZ, Quantum Fourier Transform, and Grover search) under nonstationary noise regimes simulated in Qiskit Aer, using an instrumented testbed where action labels correspond to graded mitigation intensity. Across Clifford, non-Clifford, and structured workloads, GSC-QEMit improves average logical fidelity by \textbf{+9.0\%} relative to unmitigated execution while reducing unnecessary heavy interventions by reserving them for inferred noise spikes. The resulting policies exhibit a favorable fidelity--cost trade-off and transfer across the evaluated workloads without circuit-specific tuning.

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 introduces GSC-QEMit, a telemetry-driven framework for adaptive quantum error mitigation consisting of a Growing Hierarchical Self-Organizing Map (GHSOM) for clustering streaming device telemetry into operating contexts, an uncertainty-aware subsampled Gaussian-process forecaster for short-horizon fidelity prediction, and a cost-aware contextual multi-armed bandit (CMAB) using Thompson sampling to select between lightweight and heavy mitigation actions. Evaluated on GHZ, QFT, and Grover circuits under nonstationary noise in Qiskit Aer, the method is claimed to deliver a +9.0% average logical fidelity gain relative to unmitigated execution while reducing unnecessary heavy interventions.

Significance. If the empirical claims hold under broader validation, the work offers a practical, composable approach to balancing mitigation overhead against performance in drifting noise environments, which is relevant for NISQ-era deployments. The explicit cost modeling in the bandit and the hierarchical context discovery are reasonable extensions of existing ML-for-QEM ideas, but the significance is tempered by the simulation-only setting and the absence of statistical characterization of the reported gains.

major comments (3)
  1. Abstract: the headline result of a +9.0% average logical fidelity improvement is stated without error bars, confidence intervals, statistical significance tests, baseline definitions, or data-exclusion criteria, rendering the central empirical claim difficult to assess for robustness.
  2. Evaluation (throughout): all reported results, including the fidelity-cost trade-off and policy transfer across workloads, are obtained exclusively from Qiskit Aer simulations with synthetic nonstationary noise; no experiments on physical hardware are presented, leaving open whether the learned GHSOM contexts, GP forecasts, or CMAB policies generalize when confronted with unmodeled effects such as qubit-specific drift, crosstalk, or calibration jumps.
  3. Methods (GHSOM and forecaster sections): the assumptions that GHSOM-derived contexts are semantically meaningful and that the subsampled GP reliably predicts fidelity degradation are not accompanied by ablation studies or sensitivity analyses that would demonstrate stability when the telemetry distribution deviates from the simulated regimes.
minor comments (2)
  1. Notation for the cost term inside the CMAB objective and the precise definition of 'logical fidelity' used in the reported metric should be stated explicitly in the main text rather than left to supplementary material.
  2. Figure captions and axis labels for the fidelity-vs-cost plots would benefit from clearer indication of which curves correspond to which circuit families and noise regimes.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful and constructive review of our manuscript. We address each major comment point by point below, indicating the revisions planned for the next version.

read point-by-point responses

  1. Referee: Abstract: the headline result of a +9.0% average logical fidelity improvement is stated without error bars, confidence intervals, statistical significance tests, baseline definitions, or data-exclusion criteria, rendering the central empirical claim difficult to assess for robustness.

    Authors: We agree that the abstract would benefit from additional statistical context. In the revised manuscript we will include error bars or confidence intervals for the reported +9.0% fidelity gain, explicitly state the baseline (unmitigated execution), reference the number of trials and any significance testing performed, and clarify data-exclusion criteria. These details already appear in the evaluation section and will be summarized in the abstract. revision: yes

  2. Referee: Evaluation (throughout): all reported results, including the fidelity-cost trade-off and policy transfer across workloads, are obtained exclusively from Qiskit Aer simulations with synthetic nonstationary noise; no experiments on physical hardware are presented, leaving open whether the learned GHSOM contexts, GP forecasts, or CMAB policies generalize when confronted with unmodeled effects such as qubit-specific drift, crosstalk, or calibration jumps.

    Authors: The evaluation is deliberately simulation-based to enable reproducible, controlled study of nonstationary noise and precise telemetry collection. We will add a discussion subsection addressing generalization to hardware, including how the telemetry-driven design can incorporate real-device effects and the expected impact of unmodeled phenomena. Full hardware validation lies beyond the scope of the present algorithmic study and is reserved for future work. revision: partial

  3. Referee: Methods (GHSOM and forecaster sections): the assumptions that GHSOM-derived contexts are semantically meaningful and that the subsampled GP reliably predicts fidelity degradation are not accompanied by ablation studies or sensitivity analyses that would demonstrate stability when the telemetry distribution deviates from the simulated regimes.

    Authors: We will include new ablation studies and sensitivity analyses in the revised methods and results sections. These will systematically vary telemetry noise levels and distribution shifts to quantify the stability of GHSOM context discovery and subsampled GP fidelity forecasts. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical composition of standard ML modules with simulation-based evaluation

full rationale

The paper describes GSC-QEMit as a forward composition of off-the-shelf components (GHSOM clustering of telemetry, subsampled Gaussian-process forecasting, and cost-aware CMAB with Thompson sampling) whose performance is measured empirically on Qiskit Aer simulations of GHZ, QFT, and Grover circuits. The reported +9.0% fidelity gain is an observed outcome under those simulated nonstationary noise regimes, not an algebraic identity or fitted parameter renamed as a prediction. No equations, self-citations, or uniqueness theorems are presented that would reduce the central claim to its inputs by construction. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only the abstract is available, so the ledger is limited to high-level domain assumptions stated or implied in the summary. No explicit free parameters, invented physical entities, or ad-hoc axioms are named.

axioms (2)
  • domain assumption Streaming telemetry from quantum devices can be clustered into stable operating contexts by a Growing Hierarchical Self-Organizing Map.
    Invoked by the (G) module to enable context-aware forecasting and bandit decisions.
  • domain assumption Short-horizon fidelity degradation under nonstationary noise is predictable by an uncertainty-aware subsampled Gaussian process.
    Invoked by the (S) module; central to the forecast-and-bandit loop.

pith-pipeline@v0.9.0 · 5580 in / 1586 out tokens · 122310 ms · 2026-05-08T04:00:52.692145+00:00 · methodology

discussion (0)

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Reference graph

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