论文标题
量子控制的神经网络加速器
Neural network accelerator for quantum control
论文作者
论文摘要
有效的量子控制对于使用当前技术的实用量子计算实施是必需的。用于确定最佳控制参数的常规算法在计算上是昂贵的，在很大程度上将它们排除在模拟之外。作为查找表的构成的现有硬件解决方案不精确且昂贵。通过设计机器学习模型来近似传统工具的结果，可以产生更有效的方法。然后可以将这样的模型合成为硬件加速器以用于量子系统。在这项研究中，我们演示了一种用于预测最佳脉冲参数的机器学习算法。该算法的轻巧足以适合低资源FPGA，并以175 ns的延迟和管道间隔的延迟为5 ns，$〜>〜>〜$〜>〜$ 0.99 gote fidelity。从长远来看，这种加速器可以在传统计算机无法运行的量子计算硬件附近使用，从而在低潜伏期以合理的成本实现量子控制，而不会在低温环境之外产生大型数据带宽。
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.
