  Atomic level qubits in silicon are attractive candidates for large-scale
quantum computing, however, their quantum properties and controllability are
sensitive to details such as the number of donor atoms comprising a qubit and
their precise location. This work combines machine learning techniques with
million-atom simulations of scanning-tunnelling-microscope (STM) images of
dopants to formulate a theoretical framework capable of determining the number
of dopants at a particular qubit location and their positions with exact
lattice-site precision. A convolutional neural network was trained on 100,000
simulated STM images, acquiring a characterisation fidelity (number and
absolute donor positions) of above 98\% over a set of 17,600 test images
including planar and blurring noise. The method established here will enable a
high-precision post-fabrication characterisation of dopant qubits in silicon,
with high-throughput potentially alleviating the requirements on the level of
resource required for quantum-based characterisation, which may be otherwise a
challenge in the context of large qubit arrays for universal quantum computing.
