  Quantum machine learning algorithms are expected to play a pivotal role in
quantum chemistry simulations in the immediate future. One such key application
is the training of a quantum neural network to learn the potential energy
surface and force field of molecular systems. We address this task by using the
quantum extreme learning machine paradigm. This particular supervised learning
routine allows for resource-efficient training, consisting of a simple linear
regression performed on a classical computer. We have tested a setup that can
be used to study molecules of any dimension and is optimized for immediate use
on NISQ devices with a limited number of native gates. We have applied this
setup to three case studies: lithium hydride, water, and formamide, carrying
out both noiseless simulations and actual implementation on IBM quantum
hardware. Compared to other supervised learning routines, the proposed setup
requires minimal quantum resources, making it feasible for direct
implementation on quantum platforms, while still achieving a high level of
predictive accuracy compared to simulations. Our encouraging results pave the
way towards the future application to more complex molecules, being the
proposed setup scalable.
