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Multi-class classification based on quantum state discrimination
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DOI：10.1016/j.fss.2023.03.012.png)
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Abstract
En 中文
We present a general framework for the problem of multi-class classification using classification functions that can be interpreted as fuzzy sets. We specialize these functions in the domain of Quantum-inspired classifiers, which are based on quantum state discrimination techniques. In particular, we use unsharp observables (Positive Operator-Valued Measures) that are determined by the training set of a given dataset to construct these classification functions. We show that such classifiers can be tested on nearterm quantum computers once these classification functions are distilled (on a classical platform) from the quantum encoding of a training dataset. We compare these experimental results with their theoretical counterparts and we pose some questions for future research.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Quantum -inspired algorithms
Multi -class classification
Pretty Good Measurement
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