Compare Papers

Paper 1

A manufacturable surface code architecture for spin qubits with fast transversal logic

Jason D. Chadwick, Willers Yang, Joshua Viszlai, Frederic T. Chong

Year: 2025
Journal: arXiv preprint
DOI: arXiv:2512.07131
arXiv: 2512.07131

Spin qubits in silicon quantum dot arrays are a promising quantum computation platform for long-term scalability due to their small qubit footprint and compatibility with advanced semiconductor manufacturing. However, spin qubit devices face a key architectural bottleneck: the large physical footprint of readout components relative to qubits prevents a dense layout where all qubits can be measured simultaneously, complicating the implementation of quantum error correction. This challenge is offset by the platform's unique rapid shuttling capability, which can be used to transport qubits to distant readout ports. In this work, we explore the design constraints and capabilities of spin qubits in silicon and propose the SNAQ (Shuttling-capable Narrow Array of spin Qubits) surface code architecture, which relaxes the 1:1 readout-to-qubit assumption by leveraging spin shuttling to time-multiplex ancilla qubit initialization and readout. Our analysis shows that, given sufficiently high (experimentally demonstrated) qubit coherence times, SNAQ delivers an orders-of-magnitude reduction in chip area per logical qubit. Additionally, by using a denser grid of physical qubits, SNAQ enables fast transversal logic for short-distance logical operations, achieving 4.0-22.3x improvement in local logical clock speed while still supporting global operations via lattice surgery. This translates to a 57-60% reduction in spacetime cost of 15-to-1 magic state distillation, a key fault-tolerant subroutine. Our work pinpoints critical hardware metrics and provides a compelling path toward high-performance fault-tolerant computation on near-term-manufacturable spin qubit arrays.

Open paper

Paper 2

Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes

Xiangjun Mi, Frank Mueller

Year: 2025
Journal: arXiv preprint
DOI: arXiv:2510.06257
arXiv: 2510.06257

Quantum error correction (QEC) is essential for scalable quantum computing, yet decoding errors via conventional algorithms result in limited accuracy (i.e., suppression of logical errors) and high overheads, both of which can be alleviated by inference-based decoders. To date, such machine-learning (ML) decoders lack two key properties crucial for practical fault tolerance: reliable uncertainty quantification and robust generalization to previously unseen codes. To address this gap, we propose \textbf{QuBA}, a Bayesian graph neural decoder that integrates attention to both dot-product and multi-head, enabling expressive error-pattern recognition alongside calibrated uncertainty estimates. Building on QuBA, we further develop \textbf{SAGU }\textbf{(Sequential Aggregate Generalization under Uncertainty)}, a multi-code training framework with enhanced cross-domain robustness enabling decoding beyond the training set. Experiments on bivariate bicycle (BB) codes and their coprime variants demonstrate that (i) both QuBA and SAGU consistently outperform the classical baseline belief propagation (BP), achieving a reduction of on average \emph{one order of magnitude} in logical error rate (LER), and up to \emph{two orders of magnitude} under confident-decision bounds on the coprime BB code $[[154, 6, 16]]$; (ii) QuBA also surpasses state-of-the-art neural decoders, providing an advantage of roughly \emph{one order of magnitude} (e.g., for the larger BB code $[[756, 16, \leq34]]$) even when considering conservative (safe) decision bounds; (iii) SAGU achieves decoding performance comparable to or even outperforming QuBA's domain-specific training approach.

Open paper