Neural network accelerator for quantum control

TL;DR

This paper presents a lightweight machine learning-based hardware accelerator for quantum control, enabling fast, high-fidelity pulse parameter predictions directly at quantum hardware with minimal latency and resource usage.

Contribution

It introduces a novel FPGA-implementable machine learning model that approximates traditional quantum control algorithms, reducing computational costs and latency.

Findings

Achieves inference latency of 175 ns on FPGA

Maintains >0.99 gate fidelity in predictions

Operates efficiently within cryogenic environments

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. 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.