# KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA

arXiv: https://arxiv.org/abs/2503.03544 · HTML: https://mlsanity.com/paper/2503.03544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs](https://mlsanity.com/paper/2607.08427/) (2026)

## Abstract (arXiv)

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.