Cooling and entangling ultracold atoms in optical lattices

Yang, Bing; Sun, Hui; Huang, Chun-Jiong; Wang, Han-Yi; Deng, Youjin; Dai, Han-Ning; Yuan, Zhen-Sheng; Pan, Jian-Wei

doi:10.1126/science.aaz6801

Cited by 113 publications

(79 citation statements)

References 62 publications

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“…To reduce the error, we can simply repeat this spin flipping and atom removal twice to fully clean the atoms on the gauge sites. Previous experiments [43] did not observe significant heating in this preparation stage, indicating the practicability of this scheme.…”

Section: Proposal For Future Experimentssupporting

confidence: 49%

“…[20] begins with a twodimensional unity-filled Mott insulator of ultracold bosonic 87 Rb atoms. A recently developed staggered-immersion cooling technique in optical lattices has enabled the average filling factor on 10 4 lattice sites to be 0.992(1) [43]. To initialize the state for quantum-simulating a lattice gauge theory, the experiment employs a site-selective addressing technique to remove the atoms on all of the gauge sites [44].…”

Section: Experimental Initialization Procedures and Main Defectsmentioning

confidence: 99%

“…In the initialization stage, imperfections in the state manipulation may imprint defects onto the initial state. In particular, defects in the Mott insulator may appear due to an atom splitting after the staggered-immersion cooling [43]. The basic concept of this cooling method is to transfer the entropy into contacting superfluid reservoirs, which can absorb almost all of the thermal entropy and are later removed from the system.…”

Section: Experimental Initialization Procedures and Main Defectsmentioning

confidence: 99%

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Robustness of gauge-invariant dynamics against defects in ultracold-atom gauge theories

Halimeh

Ott

McCulloch

et al. 2020

Phys. Rev. Research

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Recent years have seen strong progress in quantum simulation of gauge-theory dynamics using ultracold-atom experiments. A principal challenge in these efforts is the certification of gauge invariance, which has recently been realized [Yang et al., arXiv:2003.08945]. One major but poorly investigated experimental source of gaugeinvariance violation is an imperfect preparation of the initial state. Using the time-dependent density-matrix renormalization group, we analyze the robustness of gauge-invariant dynamics against potential preparation defects in the above ultracold-atom implementation of a U(1) gauge theory. We find defects related to an erroneous initialization of matter fields to be innocuous, as the associated gauge-invariance violation remains strongly localized throughout the time evolution. A defect due to faulty initialization of the gauge field leads to a mild proliferation of the associated violation. Furthermore, we characterize the influence of immobile and mobile defects by monitoring the spread of entanglement entropy. Overall, our results indicate that the aforementioned experimental realization exhibits a high level of fidelity in the gauge invariance of its dynamics at all evolution times. Our work provides strong evidence that ultracold-atom setups can serve as an extremely reliable framework for the quantum simulation of gauge-theory dynamics.

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Section: Proposal For Future Experimentssupporting

confidence: 49%

Section: Experimental Initialization Procedures and Main Defectsmentioning

confidence: 99%

Section: Experimental Initialization Procedures and Main Defectsmentioning

confidence: 99%

See 1 more Smart Citation

Robustness of gauge-invariant dynamics against defects in ultracold-atom gauge theories

Halimeh

Ott

McCulloch

et al. 2020

Phys. Rev. Research

Self Cite

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“…These systems can be coupled to the external input signals in the form of microwaves. Similarly, networks of trapped ions [ 155 ] and cold atoms [ 156,157 ] could also be used for implementing QRNs.…”

Section: Physical Systemsmentioning

confidence: 99%

Quantum Neuromorphic Computing with Reservoir Computing Networks

et al. 2021

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Quantum reservoir networks combine the intelligence of neural networks with the potential of quantum computing in a single platform. This platform operates on the architecture of reservoir computing, which can function even with random connections between neural nodes. This is a major advantage for hardware implementation. Herein is described how reservoir computing is brought into the quantum domain to perform various tasks, including characterization of quantum states, quantum estimation, quantum state preparation, and quantum computing. It shows quantum enhancement in classical data processing, and creates the opportunity for quantum information processing within the robust paradigm of neural networks. It is friendly for implementation in a wide range of physical systems, including quantum dots, superconductors, trapped ions, cold atoms, and exciton‐polaritons.

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“…Quantum computing and communications are areas that are developing rapidly. Putting them into practical use is just around the corner, and this is bound to elevate AI into a new age (Arute et al, 2020;Yang et al, 2020). Although quantum computing and AI have been developed in parallel and each has achieved significant results, these two fields should be able to be combined to complement each other in both theory and applications (Weigang, 1998;Sgarbas, 2007;Dunjko and Briegel, 2018).…”

Section: Introductionmentioning

confidence: 99%