Multi-Stage Mamba-Based Architecture for Fast and Scalable Superconducting Qubit Readout

• URL: http://arxiv.org/abs/2607.11668v1
• Date: Mon, 13 Jul 2026 15:12:07 GMT
• Title: Multi-Stage Mamba-Based Architecture for Fast and Scalable Superconducting Qubit Readout
• Abstract summary: In superconducting quantum processors, readout operations are both error-prone and high-latency.<n>We present a multi-stage qubit state discriminator based on the Mamba model.<n>Our model achieves a geometric mean readout fidelity of 0.906, outperforming the best-reported state-of-the-art method.
• Score: 2.7180447022087932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable qubit readout is a critical bottleneck toward fault-tolerant quantum computing (FTQC). In superconducting quantum processors, readout operations are both error-prone and high-latency. These challenges become more severe in frequency-multiplexed architectures, where signal crosstalk among neighboring qubits significantly degrades readout fidelity. Existing machine learning (ML)-based approaches rely on feed-forward neural networks (FNNs) that suffer from large parameter sizes and lack an end-to-end network that jointly addresses relaxation errors and discriminates qubit states. In this work, we present a multi-stage qubit state discriminator based on the Mamba model, which enables efficient sequence modeling with linear complexity. The first stage performs initial state discrimination, followed by a refinement stage that identifies and mitigates relaxation-induced errors. Our lightweight model achieves a geometric mean readout fidelity of 0.906, outperforming the best-reported state-of-the-art method while reducing parameter size by 49.6%; our optimal model further reaches 0.911. Both models remain robust across varying input trace lengths, maintaining a high fidelity of 0.893 at readout durations as short as 500 $ns$, achieving up to a 26% reduction in logical error rate over prior work in quantum error correction (QEC).

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