Artificial intelligence for qubit readout

Lead Research Organisation: University of Oxford
Department Name: Materials

Abstract

Controlling materials on the nanoscale is now sufficiently refined that new methods are needed for fabricating the structures and tuning their performance. In many cases we are reaching the limits of our ability to do this using human control of the process, all the more so when a key consideration is scalability for technological applications. The project identifies quantum devices where artificial intelligence will provide the key to speeding up, scaling up, and opening up new technologies

The objective is to develop radio-frequency reflectometry for fast and sensitive readout of quantum devices. To achieve optimal sensitivity in this measurement technique, which can approach the quantum limit, impedance matching between the device and the external circuitry is essential to maximize power transfer between them. Artificial intelligence, and in particular machine learning methods such a deep learning, will be used to find the right operation parameters of the radio frequency circuitry and the quantum limited amplifier.

Radio-frequency reflectometry permits rapid readout of charge sensors, quantum devices, and nanomechanical resonators, as well as quantum-dot circuits. This method has the advantage that it does not require a separate charge sensor, making it ideal for circuit scalability. However, the sensitivity is not yet sufficient to measure systems in which coherence is not preserved for relatively long periods of time. For the first time this technique for fast readout will be combined with machine learning methods to reach its full potential.

This project is key to unleash the potential of quantum technologies by allowing fast measurement of quantum devices, and thus the possibility to control technologically relevant quantum circuits.

A collaboration with University of Basel provides us with the quantum devices we require for our experiments, and a collaboration with the Department of Engineering at University of Oxford allows us to benefit from the expertise of computer scientist dedicated to the study of artificial intelligence, in particular Bayesian optimisation and other machine learning techniques which do not require large amounts of data, which is not available for quantum devices. The number of data sets required to train the machine learning models is large, and therefore our collaborators in IST Austria will provide us with a number of data sets from their laboratory.

This project falls within the EPSRC Quantum technologies research area.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513295/1 01/10/2018 30/09/2023
2281597 Studentship EP/R513295/1 01/10/2019 31/03/2023 Brandon Severin