Abstract
The development of error resistant and scalable quantum computers relies on having robust control over qubits with optimal metrics. In many qubit implementations, including semiconductor systems, these qubit metrics can be tuned by altering system control parameters. Here, we present an algorithm which enables the automatic optimisation of qubit quality factors. The algorithm performs appropriate measurements, and automatically analyses the results. It then intelligently selects the next set of device parameters to query in order to efficiently converge upon the optimum. We experimentally demonstrate the versatility of our method by optimising the properties of two different qubit parameterisations in two distinct semiconductor devices. We use Bayesian optimisation, Bayesian inference and optimal experimental design, motivated by information theory, to efficiently characterise and optimise qubit performance. This contribution represents a step towards the complete automation of the realisation and control of large scale quantum information devices.
*We acknowledge J. Zimmerman and A. C. Gossard for the growth of the AlGaAs/GaAs heterostructure. This work was supported by the Royal Society, the EPSRC National Quantum Technology Hub in Networked Quantum Information Technology (EP/M013243/1), Quantum Technology Capital (EP/N014995/1), EPSRC Platform Grant (EP/R029229/1), the European Research Council (grant agreement 818751), Graphcore, the Swiss NSF Project 179024, the Swiss Nanoscience Institute, the NCCR QSIT, the NCCR SPIN, and the EU H2020 European Microkelvin Platform EMP grant No. 824109. This publication was also made possible through support from Templeton World Charity Foundation and John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Templeton Foundations.
