KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA
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Abstract
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 qubitstate 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 32 ns. The results demonstrate that compressed neural networks can maintain highfidelity independent readout while enabling efficient hardware implementation, advancing practical quantum computing.