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arXiv 2603.04584quant-ph
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容错执行纠错量子算法

Fault-tolerant execution of error-corrected quantum algorithms

Quantinuum
Michael A. Perlin, Zichang He, Anthony Alexiades Armenakas, Pablo Andres-Martinez, Tianyi Hao, Dylan Herman, Yuwei Jin, Karl Mayer, Chris Self, David Amaro, Cia… 展开作者

Michael A. Perlin, Zichang He, Anthony Alexiades Armenakas, Pablo Andres-Martinez, Tianyi Hao, Dylan Herman, Yuwei Jin, Karl Mayer, Chris Self, David Amaro, Ciaran Ryan-Anderson, Ruslan Shaydulin

发表信息：
Phys. Rev. X, 2026
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AI总结：

本研究展示了在离子阱量子处理器上使用容错组件执行纠错量子算法，验证了容错计算的可行性与性能。

摘要：

扩大量子算法以解决科学和工业中的高影响问题需要量子纠错和容错。尽管在实验上实现了纠错基本原理方面取得了进展，但使用仅容错（FT）组件执行逻辑量子算法仍难以实现。我们演示了在Quantinuum H2和Helios离子阱量子处理器上，使用[[7,1,3]]Steane码执行两个量子算法，即量子近似优化算法（QAOA）和应用于泊松方程的Harrow-Hassidim-Lloyd（HHL）算法。对于在5和6个逻辑量子位上的QAOA电路，我们展示了通过增加QAOA层和用于近似逻辑旋转的T门数量而提高的性能，尽管物理电路复杂性增加。我们进一步表明，具有多达8个逻辑量子位和9个逻辑T门的QAOA电路与未编码电路表现相似。对于我们运行的最大的QAOA电路，具有12个逻辑（97个物理）量子位和2132个物理两量子位门，我们仍然观察到优于随机的性能。最后，我们展示了添加主动QEC循环和增加状态准备子程序的重复直到成功限制可以提高量子算法的性能，从而证明了可扩展FT量子计算的关键能力。我们的结果得益于FT逻辑T门实现的不纯度约为2.6(4)×10⁻³和具有测量依赖反馈的动态电路。我们的工作证明了使用仅FT组件执行复杂、纠错算法量子电路的近平衡性能。

英文摘要：

Scaling up quantum algorithms to tackle high-impact problems in science and industry requires quantum error correction and fault tolerance. While progress has been made in experimentally realizing error-corrected primitives, the end-to-end execution of logical quantum algorithms using only fault-tolerant (FT) components has remained out of reach. We demonstrate the FT and error-corrected execution of two quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA) and the Harrow-Hassidim-Lloyd (HHL) algorithm applied to the Poisson equation, on Quantinuum H2 and Helios trapped-ion quantum processors using the $[[7,1,3]]$ Steane code. For QAOA circuits on 5 and 6 logical qubits, we show performance improvements from increasing the number of QAOA layers and the number of $T$ gates used to approximate logical rotations, despite increased physical circuit complexity. We further show that QAOA circuits with up to 8 logical qubits and 9 logical $T$ gates perform similarly to unencoded circuits. For the largest QAOA circuits we run, with 12 logical (97 physical) qubits and 2132 physical two-qubit gates, we still observe better-than-random performance. Finally, we show that adding active QEC cycles and increasing the repeat-until-success limit of state preparation subroutines can improve the performance of a quantum algorithm, thereby demonstrating critical capabilities of scalable FT quantum computation. Our results are enabled by an FT logical $T$ gate implementation with an infidelity of $\sim 2.6(4)\times10^{-3}$ and dynamic circuits with measurement-dependent feedback. Our work demonstrates near-break-even performance of complex, error-corrected algorithmic quantum circuits using only FT components.

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