  At large scales, quantum systems may become advantageous over their classical
counterparts at performing certain tasks. Developing tools to analyse these
systems at the relevant scales, in a manner consistent with quantum mechanics,
is therefore critical to benchmarking performance and characterising their
operation. While classical computational approaches cannot perform
like-for-like computations of quantum systems beyond a certain scale, classical
high-performance computing (HPC) may nevertheless be useful for precisely these
characterisation and certification tasks. By developing open-source customised
algorithms using high-performance computing, we perform quantum tomography on a
megascale quantum photonic detector covering a Hilbert space of $10^6$. This
requires finding $10^8$ elements of the matrix corresponding to the positive
operator valued measure (POVM), the quantum description of the detector, and is
achieved in minutes of computation time. Moreover, by exploiting the structure
of the problem, we achieve highly efficient parallel scaling, paving the way
for quantum objects up to a system size of $10^{12}$ elements to be
reconstructed using this method. In general, this shows that a consistent
quantum mechanical description of quantum phenomena is applicable at everyday
scales. More concretely, this enables the reconstruction of large-scale quantum
sources, processes and detectors used in computation and sampling tasks, which
may be necessary to prove their nonclassical character or quantum computational
advantage.
