Separable and non-separable data representation for pattern discrimination
This work addresses pattern discrimination for researchers in quantum computing and machine learning, but appears incremental as it builds on existing quantum frameworks.
The authors tackled pattern recognition by introducing a quantum information theory-based workflow that is computationally efficient for real-world data, and demonstrated its utility through a simple 2D classification example.
We provide a complete work-flow, based on the language of quantum information theory, suitable for processing data for the purpose of pattern recognition. The main advantage of the introduced scheme is that it can be easily implemented and applied to process real-world data using modest computation resources. At the same time it can be used to investigate the difference in the pattern recognition resulting from the utilization of the tensor product structure of the space of quantum states. We illustrate this difference by providing a simple example based on the classification of 2D data.