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Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer
In the compression algorithm, we define a mapping from lattice QCD data of floating-point numbers to the binary coefficients that closely reconstruct the input data from a set of basis vectors.
Lossy compression of statistical data using quantum annealer
We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables.
A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer
We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer.
Image classification using quantum inference on the D-Wave 2X
To establish a benchmark for classification performance on this reduced dimensional data set, we used an AlexNet-like architecture implemented in TensorFlow, obtaining a classification score of $94. 54 \pm 0. 7 \%$.
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