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Presently, noisy intermediate-scale quantum computers encounter significant
technological challenges that make it impossible 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
successfully target quantum many-body scar states. Scars are area-law
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.
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