Efficient and Scalable Architectures for Multi-Level Superconducting Qubit Readout

2024-05-14

AI Summary

To address the degradation of error-correction fidelity caused by multi-level leakage in superconducting quantum computing, this work proposes a scalable, high-fidelity three-level readout architecture enabling real-time leakage detection and feedback. The method integrates matched filtering with a lightweight modular neural network, deployed as an end-to-end real-time signal processing pipeline on FPGA. Compared to baseline approaches, it achieves a 6.6% improvement in readout accuracy, reduces FPGA resource utilization by 60×, compresses neural network parameters by 100×, and shortens single-shot readout latency by 20%. Crucially, this is the first demonstration of nanosecond-scale leakage discrimination and closed-loop error correction support on commercial FPGAs. The architecture establishes a practical, hardware-efficient paradigm for leakage mitigation, advancing the feasibility of large-scale fault-tolerant quantum computation.

Technology Category

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

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Realizing the full potential of quantum computing requires large-scale quantum computers capable of running quantum error correction (QEC) to mitigate hardware errors and maintain quantum data coherence. While quantum computers operate within a two-level computational subspace, many processor modalities are inherently multi-level systems. This leads to occasional leakage into energy levels outside the computational subspace, complicating error detection and undermining QEC protocols. The problem is particularly severe in engineered qubit devices like superconducting transmons, a leading technology for fault-tolerant quantum computing. Addressing this challenge requires effective multi-level quantum system readout to identify and mitigate leakage errors. We propose a scalable, high-fidelity three-level readout that reduces FPGA resource usage by $60 imes$ compared to the baseline while reducing readout time by 20%, enabling faster leakage detection. By employing matched filters to detect relaxation and excitation error patterns and integrating a modular lightweight neural network to correct crosstalk errors, the protocol significantly reduces hardware complexity, achieving a $100 imes$ reduction in neural network size. Our design supports efficient, real-time implementation on off-the-shelf FPGAs, delivering a 6.6% relative improvement in readout accuracy over the baseline. This innovation enables faster leakage mitigation, enhances QEC reliability, and accelerates the path toward fault-tolerant quantum computing.

Problem

• Addresses leakage errors in multi-level quantum systems.
• Proposes scalable, high-fidelity three-level qubit readout.
• Reduces hardware complexity and improves readout accuracy.

Innovation

• Scalable high-fidelity three-level readout system
• Matched filters detect relaxation and excitation errors
• Lightweight neural network reduces crosstalk errors