Workshop: Third International Workshop on Quantum Computing Software
Authors: David Xu (Columbia University); A. Baris Ozguler, Giuseppe Di Guglielmo, Nhan Tran, and Gabriel N. Perdue (Fermi National Accelerator Laboratory); Luca Carloni (Columbia University); and Farah Fahim (Fermi National Accelerator Laboratory)
Abstract: 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. 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.
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