Reaching New Heights Together!Congratulations to the group of Natalia Ares at the University of Oxford and their collaborators on demonstrating an all-RF reflectometry quantum device tuning using a machine learning algorithm.The algorithm can tune a double quantum dot in just a few minutes without prior knowledge about the device configuration.This achievement paves the way to more scalable quantum device architectures.We are excited to help the whole team drive the field forward with innovative approaches to quantum measurements using Zurich Instruments lock-in amplifiers.
Reaching New Heights Together!Congratulations to the group of Natalia Ares at the University of Oxford and their collaborators on demonstrating an all-RF reflectometry quantum device tuning using a machine learning algorithm.The algorithm can tune a double quantum dot in just a few minutes without prior knowledge about the device configuration.This achievement paves the way to more scalable quantum device architectures.We are excited to help the whole team drive the field forward with innovative approaches to quantum measurements using Zurich Instruments lock-in amplifiers.
Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.
Abstract Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements, i.e. without resorting to current measurements. By exploiting their bandwidth and impedance matching, we demonstrate an algorithm that automatically tunes double quantum dots with only radio-frequency measurements. The tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.
Semiconductor quantum dots are at the forefront of quantum device technology. One longstanding obstacle to scalability is that multidot systems require a lengthy, complex, experimental tuning process. Here the authors introduce a machine-learning-driven algorithm for automated tuning of quantum dots. By letting the algorithm learn from experimental data, they develop a procedure that uses a small set of measurements as its input, and then automatically tunes the double-dot system to the desired charge state. This constitutes a significant step toward fully automated operation of multidot quantum systems.
Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.
Abstract Recent progress in building large-scale quantum devices for exploring quantum computing and simulation has relied upon effective tools for achieving and maintaining good experimental parameters, i.e., tuning up devices. In many cases, including quantum dot-based architectures, the parameter space grows substantially with the number of qubits, and may become a limit to scalability. Fortunately, machine learning techniques for pattern recognition and image classification, using so-called deep neural networks, have shown surprising successes for computer-aided understanding of complex systems. We propose a new paradigm for fully automated experimental initialization through a closed-loop system relying on machine learning and optimization techniques. We use deep convolutional neural networks to characterize states and charge configurations of semiconductor quantum dot arrays when only measurements of a current−voltage characteristic of transport are available. For simplicity, we model a semiconductor nanowire connected to leads and capacitively coupled to depletion gates using the Thomas−Fermi approximation and Coulomb blockade physics. We then generate labeled training data for the neural networks, and find at least 90 % accuracy for charge and state identification for single and double dots. Using these characterization networks, we can then optimize the parameter space to achieve a desired configuration of the array, a technique we call “auto-tuning”. Finally, we show how such techniques can be implemented in an experimental setting by applying our approach to an experimental dataset, and outline further problems in this domain, from using charge sensing data to extensions to full one- and two-dimensional arrays, that can be tackled with machine learning.
Semiconducting spin quantum devices present a promising avenue for advancing noisy intermediate-scale quantum (NISQ) computers. However, the complexities involved in their tuning and characterisation currently hinder their scaling. This thesis addresses these challenges by harnessing machine-learning-based approaches. Firstly, I used Gaussian processes to develop a fully automatic tuning algorithm that exclusively used radio-frequency measurements taken on millisecond timescales. Secondly, I employed Bayesian optimisation for charge sensor optimisation and automation. Both algorithms were deployed on depletion mode hole-based Ge/SiGe devices; however, they could easily be applied to other architectures. These automated approaches considerably outperformed manual tuning and represent vital building blocks for algorithms to tune larger quantum dot arrays. Next, I developed algorithms to find the ground state charge configuration in the constant capacitance model, which was orders of magnitude faster than its predecessors, permitting the simulation of much larger arrays of sixteen dots. This open-source package can be used to gain insights into the charge stability diagrams of large quantum dot arrays and facilitates further tuning algorithm development. Lastly, I present a novel application of hidden Markov models to identify and separate the state preparation, measurement and spin-flip errors presented by a pair of hot Loss Divincenzo qubits in a Si-MOS device. This approach not only unravels these error sources but also sheds light on their relationship with various parameters, such as electron temperature, thus contributing a vital understanding to the field of quantum computing.