  Quantum machine learning has the potential to computationally outperform
classical machine learning, but it is not yet clear whether it will actually be
valuable for practical problems. While some artificial scenarios have shown
that certain quantum machine learning techniques may be advantageous compared
to their classical counterpart, it is unlikely that quantum machine learning
will outclass traditional methods on popular classical datasets such as MNIST.
In contrast, dealing with quantum data, such as quantum states or circuits, may
be the task where we can benefit from quantum methods. Therefore, it is
important to develop practically meaningful quantum datasets for which we
expect quantum methods to be superior. In this paper, we propose a machine
learning task that is likely to soon arise in the real world: clustering and
classification of quantum circuits. We provide a dataset of quantum circuits
optimized by the variational quantum eigensolver. We utilized six common types
of Hamiltonians in condensed matter physics, with a range of 4 to 16 qubits,
and applied ten different ans\"{a}tze with varying depths (ranging from 3 to
32) to generate a quantum circuit dataset of six distinct classes, each
containing 300 samples. We show that this dataset can be easily learned using
quantum methods. In particular, we demonstrate a successful classification of
our dataset using real 4-qubit devices available through IBMQ. By providing a
setting and an elementary dataset where quantum machine learning is expected to
be beneficial, we hope to encourage and ease the advancement of the field.
