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

• URL: http://arxiv.org/abs/2503.03544v1
• Date: Wed, 05 Mar 2025 14:28:16 GMT
• Title: KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA
• Authors: Xiaorang Guo, Tigran Bunarjyan, Dai Liu, Benjamin Lienhard, Martin Schulz,
• Abstract summary: This paper presents KLiNQ, a novel qubit readout architecture leveraging lightweight neural networks optimized via knowledge distillation.<n>Our approach achieves around a 99% reduction in model size compared to the baseline while maintaining a qubit-state discrimination accuracy of 91%.
• Score: 1.2832548102732355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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