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arXiv 2209.08552quant-ph
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并行窗口解码实现可扩展的容错量子计算

Parallel window decoding enables scalable fault tolerant quantum computation

Luka Skoric, Dan E. Browne, Kenton M. Barnes, Neil I. Gillespie, Earl T. Campbell

AI总结:

针对量子纠错解码器随问题规模增大而变慢导致数据积压和指数级减速的问题,提出并行窗口解码方法,实现近乎任意速度的解码,将逻辑时钟减速限制为多项式级别,并在表面码上验证了不损失逻辑保真度的并行加速效果。

摘要:

大规模量子计算机在某些问题上具有超越传统计算机的计算能力。然而,量子计算机内的物理量子比特容易受到噪声和退相干的影响,必须加以纠正才能执行可靠的容错量子计算。量子纠错(QEC)为实现此类计算提供了途径。QEC持续生成连续的数据流,解码器必须以接收速率对其进行处理,在超导量子计算机中,该速率可高达1 MHz。QEC一个鲜为人知的事实是,如果解码器基础设施无法跟上处理速度,就会遇到数据积压问题,导致量子计算机运行速度呈指数级减慢。目前量子纠错的领先方法不可扩展,因为现有的解码器通常会随着问题规模的增加而运行变慢,不可避免地遭遇积压问题。即:当前容错量子计算的领先方案是不可扩展的。在此,我们展示了如何并行化解码以实现几乎任意的速度,从而消除这一可扩展性障碍。我们的并行化要求延迟一些经典的前馈决策,导致逻辑时钟速度减慢。然而,这种减速现在仅与码大小呈多项式关系,避免了指数级减速。我们在数值上展示了用于表面码的并行解码器,表明与以前的解码器相比,逻辑保真度没有明显降低,并展示了并行化加速效果。

英文摘要:

Large-scale quantum computers have the potential to hold computational capabilities beyond conventional computers for certain problems. However, the physical qubits within a quantum computer are prone to noise and decoherence, which must be corrected in order to perform reliable, fault-tolerant quantum computations. Quantum Error Correction (QEC) provides the path for realizing such computations. QEC continuously generates a continuous stream of data that decoders must process at the rate it is received, which can be as fast as 1 MHz in superconducting quantum computers. A little known fact of QEC is that if the decoder infrastructure cannot keep up, a data backlog problem is encountered and the quantum computer runs exponentially slower. Today's leading approaches to quantum error correction are not scalable as existing decoders typically run slower as the problem size is increased, inevitably hitting the backlog problem. That is: the current leading proposal for fault-tolerant quantum computation is not scalable. Here, we show how to parallelize decoding to achieve almost arbitrary speed, removing this roadblock to scalability. Our parallelization requires some classical feed forward decisions to be delayed, leading to a slow-down of the logical clock speed. However, the slow-down is now only polynomial in code size, averting the exponential slowdown. We numerically demonstrate our parallel decoder for the surface code, showing no noticeable reduction in logical fidelity compared to previous decoders and demonstrating the parallelization speedup.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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