Reading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classification to assign the qubit states `0' or `1'. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1-10%. High readout accuracy is essential for enabling high fidelity for near-term noisy quantum computers and error-corrected quantum computers of the future. Prior works have used machine-learning-assisted single-shot qubit-state classification, where a deep neural network was used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit the scalability of systems, especially if fast hardware discrimination is required. This state-of-the-art baseline design cannot be implemented on off-the-shelf FPGAs used for the control and readout of superconducting qubits in most systems, which increases the overall readout latency as discrimination has to be performed in software. In this work, we propose HERQULES, a scalable approach to improve qubit-state discrimination by using a hierarchy of matched filters in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. We achieve substantially higher readout accuracies (16.4% relative improvement) than the baseline with a scalable design that can be readily implemented on off-the-shelf FPGAs. We also show that HERQULES is more versatile and can support shorter readout durations than the baseline design without additional training overheads.

翻译：读取量子比特是量子计算中的基本操作。它将量子信息转化为经典信息，从而实现后续分类以分配量子比特状态“0”或“1”。然而，在超导量子处理器上，量子比特读取是最易出错且速度最慢的操作之一。在最先进的超导量子处理器上，读取错误率可能介于1%至10%之间。高读取精度对于实现近期的含噪量子计算机及未来纠错量子计算机的高保真度至关重要。先前的研究已采用机器学习辅助的单次量子比特状态分类，其中使用深度神经网络通过补偿串扰错误来实现更鲁棒的判别。然而，神经网络规模可能限制系统的可扩展性，特别是在需要快速硬件判别的情况下。这一当前最先进的基线设计无法在大多数系统中用于超导量子比特控制与读取的现成FPGA上实现，从而因判别必须在软件中执行而增加了整体读取延迟。在本工作中，我们提出HERQULES——一种可扩展的方法，通过结合层级匹配滤波器与显著更小且可扩展的神经网络来改进量子比特状态判别。与基线相比，我们实现了显著更高的读取精度（相对提升16.4%），且该可扩展设计可轻松在现成的FPGA上实现。我们还表明，HERQULES具有更高的通用性，能在无需额外训练开销的情况下，支持比基线设计更短的读取持续时间。