实现弦网凝聚:通用门和抽样色多项式的斐波那契任意编织

IF 15.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Zlatko K. Minev, Khadijeh Najafi, Swarnadeep Majumder, Juven Wang, Ady Stern, Eun-Ah Kim, Chao-Ming Jian, Guanyu Zhu
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引用次数: 0

摘要

一个拓扑有序的多体量子系统的显著复杂性是编码在其任意子的特征。从这种复杂性中产生的典型预测使用斐波那契弦网凝聚(Fib SNC)及其任意子:采样Fib-SNC将估计色多项式,而交换其任意子将实现通用量子计算。然而,物理上的实现仍然难以捉摸。我们介绍了一种可扩展的动态弦网制备(DSNP),它在适合近期超导处理器的可重构图上构建Fib SNC及其任意子。将DSNP方法与深层电路的复合误差缓解相结合,我们创建,测量和编织斐波那契任意子;电荷测量显示94%的精度,交换任意子产生预期的黄金比例φ,平均精度为98%。然后,我们对Fib SNC进行采样,以估计几个图的φ + 2处的色多项式。我们的结果建立了使用Fib-SNC及其任意子进行容错通用量子计算的原理证明,并针对一个经典难题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Realizing string-net condensation: Fibonacci anyon braiding for universal gates and sampling chromatic polynomials

Realizing string-net condensation: Fibonacci anyon braiding for universal gates and sampling chromatic polynomials

The remarkable complexity of a topologically ordered many-body quantum system is encoded in the characteristics of its anyons. Quintessential predictions emanating from this complexity employ the Fibonacci string net condensate (Fib SNC) and its anyons: sampling Fib-SNC would estimate chromatic polynomials while exchanging its anyons would implement universal quantum computation. However, physical realizations remained elusive. We introduce a scalable dynamical string net preparation (DSNP) that constructs Fib SNC and its anyons on reconfigurable graphs suitable for near-term superconducting processors. Coupling the DSNP approach with composite error-mitigation on deep circuits, we create, measure, and braids Fibonacci anyons; charge measurements show 94% accuracy, and exchanging the anyons yields the expected golden ratio ϕ with 98% average accuracy. We then sample the Fib SNC to estimate chromatic polynomial at ϕ + 2 for several graphs. Our results establish the proof of principle for using Fib-SNC and its anyons for fault-tolerant universal quantum computation and aim at a classically hard problem.

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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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