| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Chip, Vol 1, Iss 2, Pp 100007- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2709-4723 |
| DOI: |
10.1016/j.chip.2022.100007 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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