  Recent progress in quantum algorithms and hardware indicates the potential
importance of quantum computing in the near future. However, finding suitable
application areas remains an active area of research. Quantum machine learning
is touted as a potential approach to demonstrate quantum advantage within both
the gate-model and the adiabatic schemes. For instance, the Quantum-assisted
Variational Autoencoder has been proposed as a quantum enhancement to the
discrete VAE. We extend on previous work and study the real-world applicability
of a QVAE by presenting a proof-of-concept for similarity search in large-scale
high-dimensional datasets. While exact and fast similarity search algorithms
are available for low dimensional datasets, scaling to high-dimensional data is
non-trivial. We show how to construct a space-efficient search index based on
the latent space representation of a QVAE. Our experiments show a correlation
between the Hamming distance in the embedded space and the Euclidean distance
in the original space on the Moderate Resolution Imaging Spectroradiometer
(MODIS) dataset. Further, we find real-world speedups compared to linear search
and demonstrate memory-efficient scaling to half a billion data points.
