Shallow quantum circuits are robust hunters for quantum many-body scars

Presently, noisy intermediate-scale quantum computers encounter significant technological challenges that make it difficult to generate large amounts of entanglement. We leverage this technological constraint as a resource and demonstrate that a shallow variational eigensolver can be trained to target quantum many-body scar states successfully. Scars are low-entanglement high-energy eigenstates of quantum many-body Hamiltonians, which are sporadic and immersed in a sea of volume-law eigenstates. We show that the algorithm is robust and can be used as a versatile diagnostic tool to uncover quantum many-body scars in arbitrary physical systems.