  In the last two decades, the combination of machine learning and quantum
computing has been an ever-growing topic of interest but, to this date, the
limitations of quantum computing hardware have somewhat restricted the use of
complex multi-qubit operations for machine learning. In this paper, we
capitalize on the cyclical nature of quantum state probabilities observed on
pulsed qubits to propose a single-qubit feed forward block whose architecture
allows for classical parameters to be used in a way similar to classical neural
networks. To do this, we modulate the pulses exciting qubits to induce
superimposed rotations around the Bloch Sphere. The approach presented here has
the advantage of employing a single qubit per block. Thus, it is linear with
respect to the number of blocks, not polynomial with respect to the number of
neurons as opposed to the majority of methods elsewhere. Further, since it
employs classical parameters, a large number of iterations and updates at
training can be effected without dwelling on coherence times and the gradients
can be reused and stored if necessary. We also show how an analogy can be drawn
to neural networks using sine-squared activation functions and illustrate how
the feed-forward block presented here may be used and implemented on
pulse-enabled quantum computers.
