Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses

• URL: http://arxiv.org/abs/2606.25795v1
• Date: Wed, 24 Jun 2026 13:13:44 GMT
• Title: Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses
• Abstract summary: We employ a superconducting transmon qubit dispersively coupled to a double-cavity system.<n>We operate this detector in a regime of increased qubit-cavity coupling, resulting in Stark shifts of 4.6 MHz.<n>We experimentally validate this protocol and demonstrate single-photon detection performance comparable to previous implementations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 $\mathcal{O}(20)$ 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 $ε_{95\%} \sim 1\times10^{-14}$ 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.

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