  In almost all quantum applications, one of the key steps is to verify that
the fidelity of the prepared quantum state meets expectations. In this Letter,
we propose a new approach solving this problem using machine-learning
techniques. Compared to other fidelity estimation methods, our method is
applicable to arbitrary quantum states, the number of required measurement
settings is small, and this number does not increase with the size of the
system. For example, for a general five-qubit quantum state, only four
measurement settings are required to predict its fidelity with $\pm1\%$
precision in a nonadversarial scenario. This machine-learning-based approach
for estimating quantum state fidelity has the potential to be widely used in
the field of quantum information.
