Estimation of relaxation parameters of spin-valley qubits via readout simulations

Two dimensional (2D)-material quantum dot systems, can host multiple qubit possibilities, namely, spin, valley and the spin-valley qubits. The spin-valley qubit, often referred to as the Kramers qubit, is of special interest due to the possibility of long relaxation and coherence times. Experimentally, such long relaxation times () have been demonstrated in the bilayer graphene (BLG) platform via Elzerman single-shot readout techniques [1-4]. However, there is a lack of comprehensive synergy in explaining the experimental trends in the relaxation times of different types of qubit possibilities, especially at low magnetic fields [2-4]. Here, we present a detailed master equation-based simulation approach to mimic the Elzerman readout schemes to understand the experimental data presented and to characterize the relaxation processes. Our approach allows us to directly extract from the experimental data, the relaxation rates for individual decay processes. We then extend our analysis to unify various experimental data observed across varying conditions in the BLG platform [2-4]. Our analysis backed up by dedicated machine learning algorithms also enables the extension of the model to qubit systems in the transition metal dichalcogenide platform.



[1] Elzerman et al. Nature 430, 431–435 (2004)

[2] Ennslin et al., Arxiv, Mar 2024

[3] Stampfer, Burkard et al., Arxiv, Feb 2024

[4] Stampfer et al., Nature Communications, 2022