Pattern recognition on the quantum Bloch sphere
This work addresses pattern recognition for researchers by introducing a quantum-inspired framework, though it appears incremental as it builds on existing classifiers with a new representation.
The authors tackled pattern recognition by mapping patterns to quantum density matrices, enabling a quantum classifier (QC) that outperforms the Nearest Mean Classifier (NMC) on 2D datasets by providing additional beneficial information on classical computers.
We introduce a framework suitable for describing pattern recognition task using the mathematical language of density matrices. In particular, we provide a one-to-one correspondence between patterns and pure density operators. This correspondence enables us to: i) represent the Nearest Mean Classifier (NMC) in terms of quantum objects, ii) introduce a Quantum Classifier (QC). By comparing the QC with the NMC on different 2D datasets, we show the first classifier can provide additional information that are particularly beneficial on a classical computer with respect to the second classifier.