  Quantum neuromorphic computing (QNC) is a sub-field of quantum machine
learning (QML) that capitalizes on inherent system dynamics. As a result, QNC
can run on contemporary, noisy quantum hardware and is poised to realize
challenging algorithms in the near term. One key issue in QNC is the
characterization of the requisite dynamics for ensuring expressive quantum
neuromorphic computation. We address this issue by proposing a building block
for QNC architectures, what we call quantum perceptrons (QPs). Our proposed QPs
compute based on the analog dynamics of interacting qubits with tunable
coupling constants. We show that QPs are, with restricted resources, a quantum
equivalent to the classical perceptron, a simple mathematical model for a
neuron that is the building block of various machine learning architectures.
Moreover, we show that QPs are theoretically capable of producing any unitary
operation. Thus, QPs are computationally more expressive than their classical
counterparts. As a result, QNC architectures built our of QPs are,
theoretically, universal. We introduce a technique for mitigating barren
plateaus in QPs called entanglement thinning. We demonstrate QPs' effectiveness
by applying them to numerous QML problems, including calculating the inner
products between quantum states, energy measurements, and time-reversal.
Finally, we discuss potential implementations of QPs and how they can be used
to build more complex QNC architectures.
