Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses
Abstract
We report on an improved microwave detection protocol for dark matter candidates such as the axion and the dark photon. We employ a superconducting transmon qubit dispersively coupled to a double-cavity system, enabling quantum non-demolition measurements of the photon occupation in a relatively short-lived storage cavity. To reduce the experimental cycle time and enhance sensitivity for axion and dark-photon searches, we operate this detector in a regime of increased qubit-cavity coupling, resulting in Stark shifts of 4.6 MHz. In this regime, conventional control pulses suffer from strong frequency-detuning sensitivity and photon-number-dependent errors. We address this limitation by implementing frequency-detuning-robust π/2 pulses (obtained by machine-learning optimization) that preserve high-fidelity qubit control over a bandwidth of approximately 20 MHz. We experimentally validate this protocol and demonstrate single-photon detection performance comparable to previous implementations, despite significantly reduced qubit coherence times and storage-cavity lifetimes. Using parity-based measurement sequences combined with a Hidden Markov Model (HMM) analysis, we achieve background rates on the order of 𝒪(20) Hz. In the absence of a magnetic field, we derive exclusion limits on the dark photon model for dark matter, reaching a sensitivity to the kinetic mixing angle of ε95% ∼ 1×10−14 at 5.051 GHz. These results establish machine-learning robust control as a key enabler for faster, more scalable microwave quantum sensors for dark-matter searches.
Versions
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BibTeX
@misc{chang2026benchmarking,
title = {{Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses}},
author = {Yu-Han Chang and Ilya Moskalenko and Marko Kuzmanović and Ognjen Stanisavljević and Isak Björkman and David Díez-Ibáñez and Yikun Gu and Akash V. Dixit and Igor G. Irastorza and Gheorghe Sorin Paraoanu},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2606.25795},
eprint = {2606.25795},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.25795},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses
AU - Yu-Han Chang
AU - Ilya Moskalenko
AU - Marko Kuzmanović
AU - Ognjen Stanisavljević
AU - Isak Björkman
AU - David Díez-Ibáñez
AU - Yikun Gu
AU - Akash V. Dixit
AU - Igor G. Irastorza
AU - Gheorghe Sorin Paraoanu
PY - 2026
DA - 2026/06/24
PB - arXiv
DO - 10.48550/arxiv.2606.25795
UR - https://arxiv.org/abs/2606.25795
AB - We report on an improved microwave detection protocol for dark matter candidates such as the axion and the dark photon. We employ a superconducting transmon qubit dispersively coupled to a double-cavity system, enabling quantum non-demolition measurements of the photon occupation in a relatively short-lived storage cavity. To reduce the experimental cycle time and enhance sensitivity for axion and dark-photon searches, we operate this detector in a regime of increased qubit-cavity coupling, resulting in Stark shifts of 4.6 MHz. In this regime, conventional control pulses suffer from strong frequency-detuning sensitivity and photon-number-dependent errors. We address this limitation by implementing frequency-detuning-robust π/2 pulses (obtained by machine-learning optimization) that preserve high-fidelity qubit control over a bandwidth of approximately 20 MHz. We experimentally validate this protocol and demonstrate single-photon detection performance comparable to previous implementations, despite significantly reduced qubit coherence times and storage-cavity lifetimes. Using parity-based measurement sequences combined with a Hidden Markov Model (HMM) analysis, we achieve background rates on the order of 𝒪(20) Hz. In the absence of a magnetic field, we derive exclusion limits on the dark photon model for dark matter, reaching a sensitivity to the kinetic mixing angle of ε_95% ∼ 1×10^−14 at 5.051 GHz. These results establish machine-learning robust control as a key enabler for faster, more scalable microwave quantum sensors for dark-matter searches.
ER -