Non-standard trajectories found by machine learning for evaporative cooling of <sup>87</sup>Rb atoms

Nakamura, Ippei; Kanemura, Atsunori; Nakaso, Takumi; Yamamoto, Ryuta; Fukuhara, Takeshi

doi:10.1364/oe.27.020435

Cited by 26 publications

(22 citation statements)

References 21 publications

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“…Below we will apply the active learning scheme described above to optimize the evaporative cooling process. We note there are already several works using machine learning based methods to optimize evaporative cooling [22][23][24][25], but their methods are different from ours.…”

Section: Example: Evaporative Coolingmentioning

confidence: 96%

Active Learning Approach to Optimization of Experimental Control

Wu,

Meng,

Wen

et al. 2020

Preprint

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Section: Example: Evaporative Coolingmentioning

confidence: 96%

Active Learning Approach to Optimization of Experimental Control

Wu,

Meng,

Wen

et al. 2020

Preprint

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“…Every optimization round, the next θ point is decided by maximizing a utility function over the surrogate model (see methods IV C). BO has been applied in quantum control [31][32][33][34], as well as in experimental variational algorithms [35,36], however its use in VQE and the exploitation of its global surrogate model remains largely unexplored.…”

Section: B Bayesian Optimisation and Boismentioning

confidence: 99%

Variational quantum algorithm with information sharing

Self,

Khosla,

Smith

et al. 2021

Preprint

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We introduce a new optimisation method for variational quantum algorithms and experimentally demonstrate a 100-fold improvement in efficiency compared to naive implementations. The effectiveness of our approach is shown by obtaining multi-dimensional energy surfaces for small molecules and a spin model. Our method solves related variational problems in parallel by exploiting the global nature of Bayesian optimisation and sharing information between different optimisers. Parallelisation makes our method ideally suited to next generation of variational problems with many physical degrees of freedom. This addresses a key challenge in scaling-up quantum algorithms towards demonstrating quantum advantage for problems of real-world interest.

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“…We employed Bayesian optimisation to overcome this difficulty. The details are explained in our previous work on Bayesian optimisation of evaporative cooling [25]. To improve the signal-to-noise ratio of the fluorescence image, it is necessary to increase photon scattering, which inevitably leads to the heating of atoms.…”

Section: Automatic Optimisation Of Cooling Parametersmentioning

confidence: 99%

“…A drawback of this scheme is the relatively large number of experimental parameters required for the cooling method. However, we have tuned them efficiently using machine learning based on Bayesian optimisation [23,24,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

Single-site-resolved imaging of ultracold atoms in a triangular optical lattice

Yamamoto,

Ozawa,

Nak

et al. 2020

Preprint

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We demonstrate single-site-resolved fluorescence imaging of ultracold 87 Rb atoms in a triangular optical lattice by employing Raman sideband cooling. Combining a Raman transition at the D1 line and a photon scattering through an optical pumping of the D2 line, we obtain images with low background noise. The Bayesian optimisation of 11 experimental parameters for fluorescence imaging with Raman sideband cooling enables us to achieve single-atom detection with a high fidelity of (96.3 ± 1.3)%. Single-atom and single-site resolved detection in a triangular optical lattice paves the way for the direct observation of spin correlations or entanglement in geometrically frustrated systems.

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Non-standard trajectories found by machine learning for evaporative cooling of ⁸⁷Rb atoms

Cited by 26 publications

References 21 publications

Active Learning Approach to Optimization of Experimental Control

Active Learning Approach to Optimization of Experimental Control

Variational quantum algorithm with information sharing

Single-site-resolved imaging of ultracold atoms in a triangular optical lattice

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Non-standard trajectories found by machine learning for evaporative cooling of 87Rb atoms

Cited by 26 publications

References 21 publications

Active Learning Approach to Optimization of Experimental Control

Active Learning Approach to Optimization of Experimental Control

Variational quantum algorithm with information sharing

Single-site-resolved imaging of ultracold atoms in a triangular optical lattice

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Non-standard trajectories found by machine learning for evaporative cooling of ⁸⁷Rb atoms