Speaker
Description
Superconducting transmon qubits have emerged as powerful tools for precision sensing
applications [1, 2], particularly in the search for light dark matter candidates such as axions
and hidden photons [3–9]. These weakly interacting particles may leave detectable signatures
through their coupling to electromagnetic fields, making highly sensitive quantum devices
essential for their discovery. Transmon qubits, with their exceptional coherence properties
and strong interaction with microwave photons, offer a unique approach to detecting such
exotic weak signals.
In this contribution, we describe how transmon qubits can be employed as quantum sensors
for light dark matter detection, focusing on their role in probing weak microwave signals that
could originate from axion-photon or hidden photon conversions. We then present our efforts
to design, simulate, and validate transmon qubit parameters with the goal of developing a
light dark matter detector.
To achieve this, we employed state-of-the-art simulation techniques such as the Lumped
Oscillator Model [10] and the Energy Participation Ratio method [11] to accurately predict
the key parameters of fixed-frequency and tunable transmon qubits. These parameters include
transition frequencies, anharmonicity, and coupling strengths, all of which are crucial for
maximizing sensitivity to potential dark matter-induced signals.
We then conducted cryogenic measurements of fabricated qubits and compared their ex-
perimental performance with theoretical predictions. The measurements focused on qubit
coherence times, transition frequencies, couplings, as these properties directly impact the de-
tection sensitivity to weak electromagnetic signals. Our results indicate a strong correlation
between simulated and experimental data. However, deviations caused by fabrication-induced
inhomogeneities and setup limitations highlight the need for further refinements in device
engineering.
Future efforts will focus on improving fabrication processes and refining theoretical models
to further enhance detection sensitivity and mitigate sources of noise.
