Superconducting qubits are among the most promising candidates for building quantum information processors. Yet, they are often limited by slow and error-prone qubit readout -- a critical factor in achieving high-fidelity operations. While current methods, including deep neural networks, enhance readout accuracy, they typically lack support for mid-circuit measurements essential for quantum error correction, and they usually rely on large, resource-intensive network models. This paper presents KLiNQ, a novel qubit readout architecture leveraging lightweight neural networks optimized via knowledge distillation. Our approach achieves around a 99% reduction in model size compared to the baseline while maintaining a qubit-state discrimination accuracy of 91%. KLiNQ facilitates rapid, independent qubit-state readouts that enable mid-circuit measurements by assigning a dedicated, compact neural network for each qubit. Implemented on the Xilinx UltraScale+ FPGA, our design can perform the discrimination within 32ns. The results demonstrate that compressed neural networks can maintain high-fidelity independent readout while enabling efficient hardware implementation, advancing practical quantum computing.
@article{arxiv.2503.03544,
title = {KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA},
author = {Xiaorang Guo and Tigran Bunarjyan and Dai Liu and Benjamin Lienhard and Martin Schulz},
journal= {arXiv preprint arXiv:2503.03544},
year = {2025}
}
Comments
Accepted by the 62nd Design Automation Conference (DAC 2025)