The properties of quantum dot–based spin qubits have significant interdevice variability due to the unavoidable presence of various types of disorder in semiconductor nanostructures. A significant source of this variability is charge disorder at the semiconductor-oxide interface, which causes unpredictable, yet, as we show here, correlated fluctuations in such essential properties of quantum dots, such as their mutual tunnel couplings, and electronic confinement energies. This study presents a systematic approach to characterize and mitigate the effects of such disorder. We use finite-element modeling of a $\mathrm{Si}$/$\mathrm{Si}$-$\mathrm{Ge}$ double quantum dot to generate a large statistical ensemble of devices, simulating the impact of trapped interface charges. This work results in a predictive statistical model capable of generating realistic artificial data for training machine-learning algorithms. By applying principal component analysis to this dataset, we identify the dominant modes through which disorder affects the multidimensional parameter space of the device. Our findings show that the parameter variations are not arbitrary, but are concentrated along a few principal axes—i.e., there are significant correlations between many properties of the devices. We finally compare that against control modes generated by sweeping the gate voltages, revealing limitations of the plunger-only control. This work provides a framework for enhancing the controllability and tunability of spin qubit devices, by systematically addressing the nature of electrostatic disorder that leads to statistical correlations in properties of double quantum dots.

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• Quantum control
• Quantum fluctuations & noise
• Quantum information with solid state qubits
• Surface & interfacial phenomena
• Double quantum dots
• Semiconductor compounds
• Spin qubits
• Finite-element method