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Quantum computing has shown considerable promise for compute-intensive tasks
in recent years. For instance, classification tasks based on quantum neural
networks (QNN) have garnered significant interest from researchers and have
been evaluated in various scenarios. However, the majority of quantum
classifiers are currently limited to binary classification tasks due to either
constrained quantum computing resources or the need for intensive classical
post-processing. In this paper, we propose an efficient quantum
multi-classifier called MORE, which stands for measurement and correlation
based variational quantum multi-classifier. MORE adopts the same variational
ansatz as binary classifiers while performing multi-classification by fully
utilizing the quantum information of a single readout qubit. To extract the
complete information from the readout qubit, we select three observables that
form the basis of a two-dimensional Hilbert space. We then use the quantum
state tomography technique to reconstruct the readout state from the
measurement results. Afterward, we explore the correlation between classes to
determine the quantum labels for classes using the variational quantum
clustering approach. Next, quantum label-based supervised learning is performed
to identify the mapping between the input data and their corresponding quantum
labels. Finally, the predicted label is determined by its closest quantum label
when using the classifier. We implement this approach using the Qiskit Python
library and evaluate it through extensive experiments on both noise-free and
noisy quantum systems. Our evaluation results demonstrate that MORE, despite
using a simple ansatz and limited quantum resources, achieves advanced
performance.