Tuning arrays with rays: Physics-informed tuning of quantum dot charge states

Quantum computers based on gate-defined quantum dots (QDs) are expected to scale. However, as the number of qubits increases, the burden of manually calibrating these systems becomes unreasonable and autonomous tuning must be used. There have been a range of recent demonstrations of automated tuning...

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Bibliographic Details
Published inarXiv.org
Main Authors Ziegler, Joshua, Luthi, Florian, Ramsey, Mick, Borjans, Felix, Zheng, Guoji, Zwolak, Justyna P
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.09.2022
Subjects
Algorithms
Automation
Calibration
Cleanrooms
Couplings
Dimensional measurement
Machine learning
Modules
Physics
Quantum computers
Quantum dots
Qubits (quantum computing)
Simulation
Topology
Tuning
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Summary:Quantum computers based on gate-defined quantum dots (QDs) are expected to scale. However, as the number of qubits increases, the burden of manually calibrating these systems becomes unreasonable and autonomous tuning must be used. There have been a range of recent demonstrations of automated tuning of various QD parameters such as coarse gate ranges, global state topology (e.g. single QD, double QD), charge, and tunnel coupling with a variety of methods. Here, we demonstrate an intuitive, reliable, and data-efficient set of tools for automated global state and charge tuning in a framework deemed physics-informed tuning (PIT). The first module of PIT is an action-based algorithm that combines a machine learning (ML) classifier with physics knowledge to navigate to a target global state. The second module uses a series of one-dimensional measurements to tune to a target charge state by first emptying the QDs of charge, followed by calibrating capacitive couplings, and navigating to the target charge state. The success rate for the action-based tuning consistently surpasses \(95~\%\) on both simulated and experimental data suitable for off-line testing. The success rate for charge setting is comparable when testing with simulated data, at \(95.5(5.4)~\%\), and only slightly worse for off-line experimental tests, with an average of \(89.7(17.4)~\%\) (median \(97.5~\%\)). It's noteworthy that the high performance is demonstrated both on data from samples fabricated in an academic cleanroom as well as on an industrial 300 mm process line, further underlining the device-agnosticity of PIT. Together, these tests on a range of simulated and experimental devices demonstrate the effectiveness and robustness of PIT.
ISSN:2331-8422