  We present the enhanced feature quantum autoencoder, or EF-QAE, a variational
quantum algorithm capable of compressing quantum states of different models
with higher fidelity. The key idea of the algorithm is to define a
parameterized quantum circuit that depends upon adjustable parameters and a
feature vector that characterizes such a model. We assess the validity of the
method in simulations by compressing ground states of the Ising model and
classical handwritten digits. The results show that EF-QAE improves the
performance compared to the standard quantum autoencoder using the same amount
of quantum resources, but at the expense of additional classical optimization.
Therefore, EF-QAE makes the task of compressing quantum information better
suited to be implemented in near-term quantum devices.
