Neural network accelerator for universal quantum control

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. 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 [1]. We extend the workflow in Ref. [1] to a universal gate set and use the accelerator near quantum computing hardware where traditional computers cannot operate, enabling universal quantum control at a reasonable cost at low latencies without incurring large data bandwidths outside of the cryogenic environment.

[1] D. Xu et al., arXiv: 2208.02645, to appear in IEEE Conference Proceedings, International Workshop on Quantum Computing Software (SC22)