  Finding the precise location of quantum critical points is of particular
importance to characterise quantum many-body systems at zero temperature.
However, quantum many-body systems are notoriously hard to study because the
dimension of their Hilbert space increases exponentially with their size.
Recently, machine learning tools known as neural-network quantum states have
been shown to effectively and efficiently simulate quantum many-body systems.
We present an approach to finding the quantum critical points of the quantum
Ising model using neural-network quantum states, analytically constructed
innate restricted Boltzmann machines, transfer learning and unsupervised
learning. We validate the approach and evaluate its efficiency and
effectiveness in comparison with other traditional approaches.
