  Simulating quantum many-body systems is believed to be one of the most
promising applications of near-term noisy quantum computers. However, in the
near term, system size limitation will remain a severe barrier for applications
in materials science or strongly correlated systems. A promising avenue of
research is to combine many-body physics with machine learning for the
classification of distinct phases. In this paper, we propose a workflow that
synergizes quantum computing, many-body theory, and quantum machine
learning(QML) for studying strongly correlated systems. In particular, it can
capture a putative quantum phase transition of the stereotypical strongly
correlated system, the Hubbard model. Following the recent proposal of the
hybrid classical-quantum algorithm for the two-site dynamical mean-field
theory(DMFT), we present a modification that allows the self-consistent
solution of the single bath site DMFT. The modified algorithm can easily be
generalized for multiple bath sites. This approach is used to generate a
database of zero-temperature wavefunctions of the Hubbard model within the DMFT
approximation. We then use a QML algorithm to distinguish between the metallic
phase and the Mott insulator phase to capture the metal-to-Mott insulator phase
transition. We train a quantum convolutional neural network(QCNN) and then
utilize the QCNN as a quantum classifier to capture the phase transition
region. This work provides a recipe for application to other phase transitions
in strongly correlated systems and represents an exciting application of
small-scale quantum devices realizable with near-term technology.
