Efficient tuning of quantum devices using machine learning

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Quantum computers with a technologically relevant number of qubits seem to have the potential for ground-breaking advances in areas ranging from sensor technology to high-performance computing. There exist several competing hardware candidates to realise these large quantum computers. Silicon devices have demonstrated high-fidelity control of electron spins and achieved long relaxation and dephasing times, which make them a favourite scalable qubit candidate. One of the key challenges in scalability is the calibration and control of larger quantum systems based on Silicon; what makes this difficult is device variability and changed behaviour after thermal cycling. Even for semiconductor devices where huge progress was achieved in minimizing variations, defects are still present and difficult to estimate. As the scale of these technologies expands, traditional methods of characterization become intractable, underscoring the urgent need for efficient automation. To address this, we propose to develop a real-time Bayesian machine learning algorithm that integrates information theory with probabilistic deep-generative models. This innovative combination will enable the algorithm to intelligently select the most informative measurements to perform next, thereby streamlining the characterization process. Preliminary results indicate that our approach significantly outperforms standard grid scan methods, reducing both the number of measurements and the time required for characterization. Importantly, our work goes beyond the conventional use of machine learning for data search and analysis, and instead demonstrates its utility in automating the actual measurements necessary for device characterization. By implementing this Bayesian approach, we aim to lay the foundation for learning-based automated measurement techniques, opening the door for the large-scale, efficient deployment of quantum technologies. Due to the high cost of measuring on real quantum devices the amount of data to train machine learning models is limited. We will investigate ways on how to incorporate physical models like quantum master equation or the constant capacitance model into the machine learning approach. This project is key to unleashing the potential of quantum technologies by allowing fast tuning of quantum devices, and thus the possibility to control technologically relevant quantum circuits. This project falls within the EPSRC Quantum technologies research area. This work will take place as a cooperation between physicists and machine learning scientists within the Engineering Department of the University of Oxford. Furthermore, we will collaborate with the Max-Plank Institute for the Science of Light in Erlangen.