memboson采样的量子优势

Chip Pub Date : 2022-06-01 DOI:10.1016/j.chip.2022.100007
Jun Gao , Xiao-Wei Wang , Wen-Hao Zhou , Zhi-Qiang Jiao , Ruo-Jing Ren , Yu-Xuan Fu , Lu-Feng Qiao , Xiao-Yun Xu , Chao-Ni Zhang , Xiao-Ling Pang , Hang Li , Yao Wang , Xian-Min Jin
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引用次数: 9

摘要

量子计算机利用量子叠加来提高并行计算能力,有望超越经典计算机,并提供指数级增长的扩展。“量子优势”一词的提出,标志着人们可以在没有纠错或已知实际应用的情况下,通过在前所未有的规模上人工控制量子系统来解决经典棘手问题的关键点。玻色子采样,一个关于多模光子网络上多光子的量子演化问题,以及它的变体,被认为是一个有希望达到这一里程碑的候选者。然而,目前的光子平台在光子数和电路模式上都存在缩放问题。在此,我们提出了该问题的一种新变体——membosonsampling,利用该问题的尺度性原则上可以扩展到更大的尺度。我们在记忆电阻启发的自环光子芯片上实验验证了该方案,并在750,000模式下获得了高达56倍的多光子配准,希尔伯特空间高达10254。该结果展示了一种集成的、成本效益高的捷径,在光子系统中进入“量子优势”状态,远远超出了以前的场景,并为量子信息处理提供了一个可扩展和可控的平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum advantage with membosonsampling

Quantum computer, harnessing quantum superposition to boost a parallel computational power, promises to outperform its classical counterparts and offer an exponentially increased scaling. The term “quantum advantage” was proposed to mark the key point when people can solve a classically intractable problem by artificially controlling a quantum system in an unprecedented scale, even without error correction or known practical applications. Boson sampling, a problem about quantum evolutions of multi-photons on multimode photonic networks, as well as its variants, has been considered as a promising candidate to reach this milestone. However, the current photonic platforms suffer from the scaling problems, both in photon numbers and circuit modes. Here, we propose a new variant of the problem, membosonsampling, exploiting the scaling of the problem can be in principle extended to a large scale. We experimentally verify the scheme on a self-looped photonic chip inspired by memristor, and obtain multi-photon registrations up to 56-fold in 750,000 modes with a Hilbert space up to 10254. The results exhibit an integrated and cost-efficient shortcut stepping into the “quantum advantage” regime in a photonic system far beyond previous scenarios, and provide a scalable and controllable platform for quantum information processing.

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CiteScore
2.80
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