Emergence of a turbulent cascade in a quantum gas

Navon, Nir; Gaunt, Alexander L.; Smith, Robert P.; Hadzibabic, Zoran

doi:10.1038/nature20114

Cited by 225 publications

(227 citation statements)

References 28 publications

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“…A challenging problem concerning the closure problem is the derivation of the −7/2 power law in the momentum distribution, which was observed in a recent experiment [118]. At present, as far as we know, this power exponent has never been derived analytically.…”

Section: Application Of Closure Methods To the Gp Modelmentioning

confidence: 99%

Numerical Studies of Quantum Turbulence

2017

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We review numerical studies of quantum turbulence. Quantum turbulence is currently one of the most important problems in low temperature physics and is actively studied for superfluid helium and atomic Bose-Einstein condensates. A key aspect of quantum turbulence is the dynamics of condensates and quantized vortices. The dynamics of quantized vortices in superfluid helium are described by the vortex filament model, while the dynamics of condensates are described by the Gross-Pitaevskii model. Both of these models are nonlinear, and the quantum turbulent states of interest are far from equilibrium. Hence, numerical studies have been indispensable for studying quantum turbulence. In fact, numerical studies have contributed in revealing the various problems of quantum turbulence. This article reviews the recent developments in numerical studies of quantum turbulence. We start with the motivation and the basics of quantum turbulence and invite readers to the frontier of this research. Though there are many important topics in the quantum turbulence of superfluid helium, this article focuses on inhomogeneous quantum turbulence in a channel, which has been motivated by recent visualization experiments. Atomic Bose-Einstein condensates are a modern issue in quantum turbulence, and this article reviews a variety of topics in the quantum turbulence of condensates e.g. two-dimensional quantum turbulence, weak wave turbulence, turbulence in a spinor condensate, etc., some of which has not been addressed in superfluid helium and paves the novel way for quantum turbulence researches. Finally we discuss open problems.

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Section: Application Of Closure Methods To the Gp Modelmentioning

confidence: 99%

Numerical Studies of Quantum Turbulence

2017

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“…(6) we need to calculate the Wigner functional Eq. (7). In the interacting case, the involved functional integration can only be performed approximately.…”

Section: Measuring the Effective Hamiltonianmentioning

confidence: 99%

Extracting the Field Theory Description of a Quantum Many-Body System from Experimental Data

et al. 2020

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Quantum field theory is a powerful tool to describe the relevant physics governing complex quantum many-body systems. Here we develop a general pathway to extract the irreducible building blocks of quantum field theoretical descriptions and its parameters purely from experimental data. This is accomplished by extracting the one-particle irreducible (1PI) vertices from which one can construct all observables. To match the capabilities of experimental techniques used in quantum simulation experiments, our approach employs a formulation of quantum field theory based on equal-time correlation functions only. We illustrate our procedure by applying it to the quantum sine-Gordon model in thermal equilibrium. The theoretical foundations are illustrated by estimating the irreducible vertices at equal times both analytically and using numerical simulations. We then demonstrate explicitly how to extract these quantities from an experiment where we quantum simulate the sine-Gordon model by two tunnel-coupled superfluids. We extract the full two-point function and the interaction vertex (four-point function) and their variation with momentum, encoding the 'running' of the couplings. The measured 1PI vertices are compared to the theoretical estimates, verifying our procedure. Our work opens new ways of addressing fundamental questions in quantum field theory, which are relevant in high-energy and condensed matter physics, and in taking quantum phenomena from fundamental science to practical technology. arXiv:1909.12815v1 [cond-mat.quant-gas]

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“…Bose-Einstein condensation in a dilute atomic vapor was first realized in 1995, resulting in the award of the Nobel Prize in 2001 [8,9]. Since then, ultracold atomic vapor experiments have been used to investigate a wide range of physical phenomena, including quantum many-body physics [10], quantum-mechanical phase transitions [11,12] and superfluid turbulence [13].…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning optimization methods to the production of a quantum gas

Barker

Style

Luksch

et al. 2020

Mach. Learn.: Sci. Technol.

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We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose-Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.

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Emergence of a turbulent cascade in a quantum gas

Cited by 225 publications

References 28 publications

Numerical Studies of Quantum Turbulence

Numerical Studies of Quantum Turbulence

Extracting the Field Theory Description of a Quantum Many-Body System from Experimental Data

Applying machine learning optimization methods to the production of a quantum gas

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