  Quantum machine learning (QML) leverages the potential from machine learning
to explore the subtle patterns in huge datasets of complex nature with quantum
advantages. This exponentially reduces the time and resources necessary for
computations. QML accelerates materials research with active screening of
chemical space, identifying novel materials for practical applications and
classifying structurally diverse materials given their measured properties.
This study analyzes the performance of three efficient quantum machine learning
algorithms viz., variational quantum eigen solver (VQE), quantum support vector
machine (QSVM) and quantum neural networks (QNN) for the classification of
transition metal chalcogenides and oxides (TMCs &TMOs). The analysis is
performed on three datasets of different sizes containing 102, 192 and 350
materials with TMCs and TMOs labelled as +1 and -1 respectively. By employing
feature selection, classical machine learning achieves 100% accuracy whereas
QML achieves the highest performance of 99% and 98% for test and train data
respectively on QSVC. This study establishes the competence of QML models in
materials classification and explores the quantum circuits in terms of
over-fitting using the circuit descriptors expressibility and entangling
capability. In addition, the perspectives on QML in materials research with
noisy intermediate scale quantum (NISQ) devices is given.
