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arXiv 2310.15313quant-ph
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如何为容错量子计算机选择解码器?速度与精度的权衡

How to choose a decoder for a fault-tolerant quantum computer? The speed vs accuracy trade-off

Nicolas Delfosse, Andres Paz, Alexander Vaschillo, Krysta M. Svore

AI总结:

本文分析了容错量子计算中解码器速度与精度的权衡,提出了一种选择解码器最佳停止时间的策略和协议,以最小化给定深度逻辑计算中每个逻辑门的时空成本,并评估了PyMatching解码器的性能与改进方向。

摘要:

实现实用量子优势需要经典解码算法在计算过程中识别并纠正错误。这种经典解码算法必须兼具精度和速度,但应以何种组合实现?解码器何时才算“足够快”或“足够精确”?在表面码方面,已提出数十种解码算法,具有不同的精度和速度。然而,目前尚不清楚如何为给定的量子架构选择最佳解码器。是否应该以降低精度为代价使用更快的解码器?或者解码器是否应该牺牲精度以适应给定的时间限制?如果解码器太慢,它可能会在达到时间限制时被停止,代价是出现一些超时失败和增加的失败率。那么解码器的最佳停止时间是多少?通过分析速度与精度的权衡,我们提出了为不同任务选择解码器最佳停止时间的策略。我们设计了一种协议,用于选择在给定深度的逻辑计算中最小化每个逻辑门时空成本的解码器。我们的协议能够比较不同的解码器,并为给定的容错量子计算架构选择合适的解码器。我们以配备桌面版PyMatching解码器的表面码为例说明了我们的协议。我们估计PyMatching足够快,能够实现数千个逻辑门,且精度优于物理量子比特。然而,我们发现,在某些假设下,由于解码延迟迫使量子比特闲置并在闲置期间积累错误,它不够快以实现10^5个逻辑门。我们期望通过在更好的机器上运行或减少操作系统干扰,PyMatching有可能得到进一步改进。

英文摘要:

Achieving practical quantum advantage requires a classical decoding algorithm to identify and correct faults during computation. This classical decoding algorithm must deliver both accuracy and speed, but in what combination? When is a decoder "fast enough" or "accurate enough"? In the case of surface codes, tens of decoding algorithms have been proposed, with different accuracies and speeds. However, it has been unclear how to choose the best decoder for a given quantum architecture. Should a faster decoder be used at the price of reduced accuracy? Or should a decoder sacrifice accuracy to fit within a given time constraint? If a decoder is too slow, it may be stopped upon reaching a time bound, at the price of some time-out failures and an increased failure rate. What then is the optimal stopping time of the decoder? By analyzing the speed vs. accuracy tradeoff, we propose strategies to select the optimal stopping time for a decoder for different tasks. We design a protocol to select the decoder that minimizes the spacetime cost per logical gate, for logical computation of a given depth. Our protocol enables comparison of different decoders, and the selection of an appropriate decoder for a given fault-tolerant quantum computing architecture. We illustrate our protocol for the surface code equipped with a desktop implementation of the PyMatching decoder. We estimate PyMatching is fast enough to implement thousands of logical gates with a better accuracy than physical qubits. However, we find it is not sufficiently fast to reach 10^5 logical gates, under certain assumptions, due to the decoding delay which forces qubits to idle and accumulate errors while idling. We expect further improvements to PyMatching are possible by running it on a better machine or by reducing the OS interference.

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