In this work, we take into consideration a quantum circuit which is based on the Variational Quantum Eigensolver (VQE) and so-called SWAP-Test what allows us to solve a classification problem for distributed data -- there are only two classes, but samples form many clusters which directly neighbor to clusters of samples from another class. The classical data observations are converted into normalized quantum states. After this operation, samples may be processed by a circuit of quantum gates. The VQE approach allows training the parameters of a quantum circuit (so-called ansatz) to output pattern-states for each class. In the utilized data set, two classes may be observed, however, the VQE circuit differentiates more classes than two (introduces more detailed cases because the samples are distributed) and the final results are obtained with the use of aforementioned SWAP-Test. The combination of the VQE and the SWAP-Test allows for the construction of a flexible system where various data sets may be classified by changing parameters of the VQE circuit. The elaborated solution is compact and requires only logarithmically increasing number of qubits (due to the exponential capacity of quantum registers). All calculations, simulations, plots, and comparisons were implemented and conduced in the Python language environment. Source codes for each example of quantum classification can be found in the source code repository.