Adaptive detection of Rabi signals under composite hypotheses
Abstract
Motivated by searches for weak coherent drives, we formulate repeated quantum sensing with a fixed shot budget as an asymmetric composite hypothesis test. Taking Rabi sensing as a concrete example, we benchmark detection power, sensitivity, and Type-II error exponents in the resonant case with unknown signal amplitude and phase. We compare non-adaptive population and transverse readouts with a myopic Bayesian policy that selects each projective axis by maximizing the expected information gain in one step. The common decision statistic is a log Bayes factor, with a policy specific threshold calibrated under the null to enforce a common Type-I error. A weak signal expansion shows that population readout is phase independent but quadratic in amplitude, giving n−1/4 sensitivity, whereas transverse readout is linear in amplitude and permits n−1/2 sensitivity without adaptation, but is phase-dependent. In Monte Carlo pseudoexperiments, the adaptive policy exploits posterior information about the unknown direction to guide subsequent readouts; its sensitivity is consistent with n−1/2 over the simulated large n range and, at the largest simulated shot counts, outperforms the fixed transverse schedules. Its phase-averaged effective Type-II exponent also exceeds the non-adaptive references over the simulated range. These results demonstrate the finite budget value of exploiting nuisance parameter information under calibrated false positive control.
Related
- Photonic-Based Adaptive Spectrum Analysis of RF Signals. Application to Signal Detection by Generalized Time-Frequency Filtering
- Deep Learning Detection of Beyond-General-Relativity Deviations in Gravitational-Wave Signals: A Detection-Threshold Study with Real LIGO Noise
- Detection of signals in presence of noise through Josephson junction switching currents
- Profiling a Rydberg-Atom Electric Field Sensor for Off-Resonant Detection of Sub-100 MHz RF Signals
- Discrete quadrature spectrum of composite bosons: Weyl relation, intensity correlations, and binned homodyne detection
BibTeX
@misc{chigusa2026adaptive,
title = {{Adaptive detection of Rabi signals under composite hypotheses}},
author = {So Chigusa},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16119},
eprint = {2609.16119},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16119},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Adaptive detection of Rabi signals under composite hypotheses
AU - So Chigusa
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.16119
UR - https://arxiv.org/abs/2609.16119
AB - Motivated by searches for weak coherent drives, we formulate repeated quantum sensing with a fixed shot budget as an asymmetric composite hypothesis test. Taking Rabi sensing as a concrete example, we benchmark detection power, sensitivity, and Type-II error exponents in the resonant case with unknown signal amplitude and phase. We compare non-adaptive population and transverse readouts with a myopic Bayesian policy that selects each projective axis by maximizing the expected information gain in one step. The common decision statistic is a log Bayes factor, with a policy specific threshold calibrated under the null to enforce a common Type-I error. A weak signal expansion shows that population readout is phase independent but quadratic in amplitude, giving n^−1/4 sensitivity, whereas transverse readout is linear in amplitude and permits n^−1/2 sensitivity without adaptation, but is phase-dependent. In Monte Carlo pseudoexperiments, the adaptive policy exploits posterior information about the unknown direction to guide subsequent readouts; its sensitivity is consistent with n^−1/2 over the simulated large n range and, at the largest simulated shot counts, outperforms the fixed transverse schedules. Its phase-averaged effective Type-II exponent also exceeds the non-adaptive references over the simulated range. These results demonstrate the finite budget value of exploiting nuisance parameter information under calibrated false positive control.
ER -