Early Fault-Tolerant Quantum Computing

Amara Katabarwa, Katerina Gratsea, Athena Caesura, Peter D. Johnson
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Abstract

In recent years, research in quantum computing has largely focused on two approaches: near-term intermediate-scale quantum (NISQ) computing and future fault-tolerant quantum computing (FTQC). A growing body of research into early fault-tolerant quantum computing (EFTQC) is exploring how to utilize quantum computers during the transition between these two eras. However, without agreed-upon characterizations of this transition, it is unclear how best to utilize EFTQC architectures. We argue for the perspective that this transition period will be characterized by a law of diminishing returns in quantum error correction (QEC), where the ability of the architecture to maintain quality operations at scale determines the point of diminishing returns. Two challenges emerge from this picture: how to model this phenomenon of diminishing return of QEC as the performance of devices is continually improving and how to design algorithms to make the most use of these devices. To address these challenges, we present models for the performance of EFTQC architectures, capturing the diminishing returns of QEC. We then use these models to elucidate the regimes in which algorithms suited to such architectures are advantageous. As a concrete example, we show that for the canonical task of phase estimation, in a regime of moderate scalability and using just over one million physical qubits, the “reach” of the quantum computer can be extended (compared to the standard approach) from 90-qubit instances to over 130-qubit instances using a simple early fault-tolerant quantum algorithm, which reduces the number of operations per circuit by a factor of 100 and increases the number of circuit repetitions by a factor of 10 000. This clarifies the role that such algorithms might play in the era of limited-scalability quantum computing.

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早期容错量子计算
近年来,量子计算研究主要集中在两种方法上:近期中等规模量子计算(NISQ)和未来容错量子计算(FTQC)。越来越多的早期容错量子计算(EFTQC)研究正在探索如何在这两个时代之间的过渡时期利用量子计算机。然而,由于没有对这一过渡时期的特征达成共识,因此还不清楚如何才能最好地利用 EFTQC 体系结构。我们认为,在量子纠错(QEC)中,这一过渡时期将以收益递减规律为特征,在这一规律下,架构保持大规模高质量运行的能力决定了收益递减点。在这一背景下,我们面临着两个挑战:如何在设备性能不断提高的情况下对 QEC 的收益递减现象进行建模,以及如何设计算法以充分利用这些设备。为了应对这些挑战,我们提出了 EFTQC 架构的性能模型,以捕捉 QEC 的收益递减现象。然后,我们利用这些模型来阐明适合此类架构的算法在哪些情况下具有优势。作为一个具体的例子,我们展示了对于相位估计这一典型任务,在中等可扩展性和使用刚刚超过一百万物理量子比特的情况下,使用一种简单的早期容错量子算法,量子计算机的 "覆盖范围"(与标准方法相比)可以从 90 量子比特实例扩展到超过 130 量子比特实例,这种算法将每个电路的操作次数减少了 100 倍,将电路重复次数增加了 10 000 倍。这阐明了这种算法在有限可扩展性量子计算时代可能发挥的作用。
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