  Efficient quantum control is necessary for practical quantum computing
implementations with current technologies. Conventional algorithms for
determining optimal control parameters are computationally expensive, largely
excluding them from use outside of the simulation. Existing hardware solutions
structured as lookup tables are imprecise and costly. By designing a machine
learning model to approximate the results of traditional tools, a more
efficient method can be produced. Such a model can then be synthesized into a
hardware accelerator for use in quantum systems. In this study, we demonstrate
a machine learning algorithm for predicting optimal pulse parameters. This
algorithm is lightweight enough to fit on a low-resource FPGA and perform
inference with a latency of 175 ns and pipeline interval of 5 ns with $~>~$0.99
gate fidelity. In the long term, such an accelerator could be used near quantum
computing hardware where traditional computers cannot operate, enabling quantum
control at a reasonable cost at low latencies without incurring large data
bandwidths outside of the cryogenic environment.
