High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Aug 13, 202011 views
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 [,1,] is touted as a potential approach to demonstrate quantum advantage within both the,gate-model [,2,,,3,] and the adiabatic [,4,,,5,] schemes. For instance, the,Quantum-assisted Variational Autoencoder (QVAE) [,6,] has been,proposed as a quantum enhancement to the discrete VAE [,7,]. 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.