StableShots: Online Shot Stopping for Quantum Circuit Execution

• URL: http://arxiv.org/abs/2606.22170v1
• Date: Sat, 20 Jun 2026 17:58:12 GMT
• Title: StableShots: Online Shot Stopping for Quantum Circuit Execution
• Abstract summary: We present StableShots, a black-box online stopping rule for static quantum circuits.<n>We evaluate StableShots on 180 QSimBench traces spanning six circuit families, six sizes from 4 to 14 qubits, and five noisy IBM simulated backends.
• Score: 40.18088847775028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum circuit execution estimates output distributions by repeated measurements, yet developers commonly choose a fixed shot budget before execution. This static choice is brittle: low budgets can under-sample the distribution, while high budgets waste measurements. In this paper, we present StableShots, a black-box online stopping rule for static quantum circuits. The method executes a fixed circuit in small batches, monitors the total-variation distance between cumulative empirical distributions, and stops after repeated evidence of local stability. We evaluate StableShots on 180 QSimBench traces spanning six circuit families, six sizes from 4 to 14 qubits, and five noisy IBM simulated backends. With validation-only calibration and 100 repeated backend-holdout splits, the selected configuration reaches TVD <= 0.05 on all held-out test evaluations with median 7,650 shots, whereas fixed-shot baselines either fail more often or spend substantially more shots.

Related papers

• Robust CHSH Self-Testing with Finite-Energy GKP States [42.71287663143178]
  We present a full-oscillator analysis of a finite-energy GKP CHSH test.<n>The observed score yields robust Bell-pair self-testing.
  arXiv  Detail & Related papers  (2026-08-11T16:33:28Z)
• Conformal Calibration for Multi-Modal Regression with Missing Modalities [0.0]
  Prediction intervals for multi-modal regression are difficult to calibrate when sources disagree or one is missing.<n>We address this through a modality-aware conformal calibration layer.<n>The layer trains or reuses one predictor per modality, computes a disagreement score from their predictions, and uses that score in split conformal calibration.
  arXiv  Detail & Related papers  (2026-08-07T22:38:35Z)
• Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs [9.68624792431081]
  We introduce Recurrent Residual Quantization (RRQ), a post-training quantization framework.<n>RRQ represents weights as a low-bit quantized base together with a sequence of quantized residual corrections.<n>In our Qwen3-8B setup, the full all-RTN 2-/4-/6-/8-bit package is constructed in 1,293 seconds, 3.3 times faster than the measured MatGPTQ construction.
  arXiv  Detail & Related papers  (2026-08-04T08:32:05Z)
• How Many Shots Are Enough for a Quantum Circuit? [40.18088847775028]
  Quantum algorithms require repeated circuit executions, known as shots, to estimate output distributions accurately.<n>We address the shot optimisation problem in a black-box setting, where no assumptions are made about the structure of the quantum circuit or the noise model of the backend.<n>We introduce IncrementalExecution, a novel online framework that dynamically determines when to stop executing shots.
  arXiv  Detail & Related papers  (2026-06-15T17:02:51Z)
• Circuit-Level Noise Estimation via Shuttling in Plaquette Circuits [46.3492821660498]
  We develop and run surface code plaquette experiments using two syndrome qubit configurations.<n>To validate our approach, we compile plaquettes to ion-trap (IonQ Aria1) native gate set and apply hardware-aware rewrite templates.<n>We estimate circuit-level noise rates from the resulting single-shot plaquette measurement statistics.
  arXiv  Detail & Related papers  (2026-06-03T09:02:32Z)
• Experimental Implementation of the Quantum Volunteer's Dilemma on NISQ Hardware: Noise Analysis and Digital-Twin Validation [0.0]
  We present an experimental implementation of the multiplayer Quantum Volunteer's Dilemma on noisy quantum (NISQ) hardware.<n>The game is evaluated for N = 2 to 9 players under four transpiler optimization levels.<n>With readout correction, the global average payoff reproduces the quantum theoretical benchmark exactly for N = 6.
  arXiv  Detail & Related papers  (2026-05-29T00:10:35Z)
• Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling [45.88028371034407]
  Post-training quantization (PTQ) is a practical path to deploy large diffusion models.<n>We propose Q-Drift, a principled sampler-side correction that treats quantization error as an implicit perturbation.<n>Q-Drift estimates a time-wise variance statistic from calibration, in practice requiring as few as 5 full-precision/quantized calibration runs.
  arXiv  Detail & Related papers  (2026-03-18T10:19:36Z)
• Faster Probabilistic Error Cancellation [6.418044102466421]
  We propose a new method to perform PEC, which results in a lower sampling cost than the standard way.<n>We show the saving both analytically and numerically over the standard PEC.<n>We also demonstrated this method experimentally and found excellent agreement between the mitigated and the ideal values.
  arXiv  Detail & Related papers  (2025-06-04T21:38:56Z)
• Leakage in restless quantum gate calibration [0.5439020425819]
  We develop a simulator of restless circuit execution based on a Markov Chain to study the effect of leakage. We show that restless calibration tolerates up to 0.5% of leakage which is large compared to the $10-4$ gate fidelity of modern single-qubit gates. Our results are obtained with standard qubit state discrimination showing that restless circuit execution is resilient against misclassified non-computational states.
  arXiv  Detail & Related papers  (2023-04-18T20:59:16Z)
• HAWQV3: Dyadic Neural Network Quantization [73.11579145354801]
  Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values. We present HAWQV3, a novel mixed-precision integer-only quantization framework.
  arXiv  Detail & Related papers  (2020-11-20T23:51:43Z)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.