A machine learning framework has been created to precisely locate atom-sized quantum bits in silicon – a crucial step for building a large-scale silicon quantum computer

By Dr Muhammad Usman and Professor Lloyd Hollenberg, University of Melbourne

Extract from Pursuit. Read the full article:

In contrast to today’s classical computers, where information is encoded in bits (0 or 1), quantum computers process information stored in quantum bits (qubits). These are hosted by quantum mechanical objects like electrons, the negatively charged particles of an atom.

Different research groups in the world are pursuing different kinds of qubits.  Some qubits offer potential for scalability, while others come with very long coherence times, that is the time for which quantum information can be robustly stored.

Qubits in silicon are highly promising as they offer both. Therefore, these qubits are one of the front-runner candidates for the design and implementation of a large-scale quantum computer architecture.

One way to implement large-scale quantum computer architecture in silicon is by placing individual phosphorus atoms on a two-dimensional grid.

However, even with state-of-the-art fabrication technologies, placing phosphorus atoms at precise locations in silicon lattice is a very challenging task. Small variations, of the order of one atomic lattice site, in their positions are often observed and may have a huge impact on the efficiency of two qubit operations.

So in 2016, we worked with the Center for Quantum Computation & Communication Technology researchers at the University of New South Wales, to develop a technique that could pinpoint exact locations of phosphorus atoms in silicon.

The technique, reported in Nature Nanotechnology, was the first to use computed scanning tunneling microscope (STM) images of phosphorus atom wave functions to pinpoint their spatial locations in silicon.

Recognising that the principle underpinning the established spatial metrology of qubit atoms is basically recognising and classifying feature maps of STM images, we decided to train a CNN (a branch of machine learning known as convolutional neural network (CNN) – an extremely powerful tool for image recognition and classification problems).

When a CNN is trained on thousands of sample images, it can precisely recognize unknown images (including noise) and perform classifications on the computed STM images. The work is published in the NPJ Computational Materials journal.

The CNN classified the test images with an accuracy of above 98 per cent, confirming that this machine learning-based technique could process qubit measurement data with high-throughput, high precision, and minimal human interaction.

This technique also has the potential to scale up for qubits consisting of more than one phosphorus atoms, where the number of possible image configurations would exponentially increase. However, machine learning-based framework could readily include any number of possible configurations.

This work shows how machine learning techniques such as developed in this work could play a crucial role in this aspect of the realisation of a full-scale fault-tolerant universal quantum computer – the ultimate goal of the global research effort.