  Highly excited eigenstates of quantum many-body systems are typically
featureless thermal states. Some systems, however, possess a small number of
special, low-entanglement eigenstates known as quantum scars. We introduce a
quantum-inspired machine learning platform based on a Quantum Variational
Autoencoder (QVAE) that detects families of scar states in spectra of many-body
systems. Unlike a classical autoencoder, QVAE performs a parametrized unitary
operation, allowing us to compress a single eigenstate into a smaller number of
qubits. We demonstrate that the autoencoder trained on a scar state is able to
detect the whole family of scar states sharing common features with the input
state. We identify families of quantum many-body scars in the PXP model beyond
the $\mathbb{Z}_2$ and $\mathbb{Z}_3$ families and find dynamically decoupled
subspaces in the Hilbert space of disordered, interacting spin ladder model.
The possibility of an automatic detection of subspaces of scar states opens new
pathways in studies of models with a weak breakdown of ergodicity and
fragmented Hilbert spaces.
