Learning Midcircuit Measurement Backaction from Three Repeated Measurements
Abstract
Accurate modeling of midcircuit measurements (MCMs) is essential for dynamic-circuit operations such as syndrome extraction, measurement-based reset, and the separation of state-preparation and measurement error. Unlike terminal measurement, a noisy MCM both produces a classical outcome and alters the incoming quantum state, thereby influencing subsequent circuit operations. This makes conventional confusion-matrix or fidelity-level characterization insufficient. Here, we introduce an efficient, self-consistent protocol for learning a single-qubit Z-twirled MCM instrument, retaining the readout-backaction correlations and excitation-decay asymmetry that are erased in Pauli-error descriptions. Remarkably, readout bit strings from only three repeated MCMs on a maximally mixed input determine all learnable parameters of the reduced instrument, up to a single unidentifiable gauge degree of freedom. Physicality constraints convert this nonidentifiability into narrow, gauge-aware error intervals. Implemented on IBM superconducting processors, the learned instrument improves Pauli-observable prediction by ∼ 100× over a conventional confusion-matrix model and reveals a T1-decay dominated backaction. Our protocol provides a compact characterization layer for state-preparation and measurement error separation, reset optimization, and noise-aware quantum error correction.
Versions
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BibTeX
@misc{chu2026learning,
title = {{Learning Midcircuit Measurement Backaction from Three Repeated Measurements}},
author = {Chia-Tung Chu and Su-un Lee and Han Zheng and Senrui Chen and Bibek Pokharel and Alireza Seif and Liang Jiang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.00433},
eprint = {2606.00433},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.00433},
}
RIS
TY - UNPB
TI - Learning Midcircuit Measurement Backaction from Three Repeated Measurements
AU - Chia-Tung Chu
AU - Su-un Lee
AU - Han Zheng
AU - Senrui Chen
AU - Bibek Pokharel
AU - Alireza Seif
AU - Liang Jiang
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2606.00433
UR - https://arxiv.org/abs/2606.00433
AB - Accurate modeling of midcircuit measurements (MCMs) is essential for dynamic-circuit operations such as syndrome extraction, measurement-based reset, and the separation of state-preparation and measurement error. Unlike terminal measurement, a noisy MCM both produces a classical outcome and alters the incoming quantum state, thereby influencing subsequent circuit operations. This makes conventional confusion-matrix or fidelity-level characterization insufficient. Here, we introduce an efficient, self-consistent protocol for learning a single-qubit Z-twirled MCM instrument, retaining the readout-backaction correlations and excitation-decay asymmetry that are erased in Pauli-error descriptions. Remarkably, readout bit strings from only three repeated MCMs on a maximally mixed input determine all learnable parameters of the reduced instrument, up to a single unidentifiable gauge degree of freedom. Physicality constraints convert this nonidentifiability into narrow, gauge-aware error intervals. Implemented on IBM superconducting processors, the learned instrument improves Pauli-observable prediction by ∼ 100× over a conventional confusion-matrix model and reveals a T_1-decay dominated backaction. Our protocol provides a compact characterization layer for state-preparation and measurement error separation, reset optimization, and noise-aware quantum error correction.
ER -