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

2025-03-05

AI Summary

Slow and error-prone readout of superconducting qubits limits high-fidelity quantum operations and mid-circuit measurements. Method: We propose a lightweight neural network architecture optimized for FPGA deployment, introducing knowledge distillation—applied here for the first time—to compress quantum readout models and enable single-qubit dedicated discriminators. Our approach integrates a compact CNN design, hardware-software co-optimization targeting Xilinx UltraScale+ FPGAs, and a low-latency inference pipeline. Contribution/Results: Compared to conventional deep networks, our model reduces parameter count by 99%, achieves a per-qubit discrimination latency of only 32 ns, and attains 91% classification accuracy. It supports high-speed, independent, and scalable parallel readout across multiple qubits. This addresses the dual requirements of real-time processing and hardware efficiency essential for practical quantum error correction.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Improves qubit readout speed and accuracy
• Enables mid-circuit measurements for error correction
• Reduces model size for efficient FPGA implementation

Innovation

• Lightweight neural networks via knowledge distillation
• Dedicated compact neural network per qubit
• FPGA implementation with 32ns discrimination time