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In the last two decades, the combination of machine learning and quantumcomputing has been an ever-growing topic of interest but, to this date, thelimitations of quantum computing hardware have somewhat restricted the use ofcomplex multi-qubit operations for machine learning. In this paper, wecapitalize on the cyclical nature of quantum state probabilities observed onpulsed qubits to propose a single-qubit feed forward block whose architectureallows for classical parameters to be used in a way similar to classical neuralnetworks. To do this, we modulate the pulses exciting qubits to inducesuperimposed rotations around the Bloch Sphere. The approach presented here hasthe advantage of employing a single qubit per block. Thus, it is linear withrespect to the number of blocks, not polynomial with respect to the number ofneurons as opposed to the majority of methods elsewhere. Further, since itemploys classical parameters, a large number of iterations and updates attraining can be effected without dwelling on coherence times and the gradientscan be reused and stored if necessary. We also show how an analogy can be drawnto neural networks using sine-squared activation functions and illustrate howthe feed-forward block presented here may be used and implemented onpulse-enabled quantum computers.

On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks