01635nas a2200181 4500008004100000245010500041210006900146260001500215300001200230490000600242520106000248100001801308700002001326700002501346700002101371700002401392856003701416 2022 eng d00aCombining machine learning with physics: A framework for tracking and sorting multiple dark solitons0 aCombining machine learning with physics A framework for tracking c06/01/2022 a023163 0 v43 a

In ultracold-atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated subcategories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a longitudinal soliton. Our trained implementation of this framework, SolDet, is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold-atom images when trained on a suitable user-provided dataset.

1 aGuo, Shangjie1 aKoh, Sophia, M.1 aFritsch, Amilson, R.1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2111.0488101358nas a2200157 4500008004100000245008900041210006900130260001500199520084100214100002501055700001801080700002001098700002101118700002401139856003701163 2022 eng d00aDark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research0 aDark Solitons in BoseEinstein Condensates A Dataset for Manybody c05/17/20223 a

We establish a dataset of over 1.6×104 experimental images of Bose-Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and object detector as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.

1 aFritsch, Amilson, R.1 aGuo, Shangjie1 aKoh, Sophia, M.1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2205.0911401332nas a2200157 4500008004100000245007400041210006900115260001500184300001200199490000600211520085200217100002201069700002501091700002101116856003701137 2021 eng d00aFeedback-stabilized dynamical steady states in the Bose-Hubbard model0 aFeedbackstabilized dynamical steady states in the BoseHubbard mo c12/15/2021 a043075 0 v33 a

The implementation of a combination of continuous weak measurement and classical feedback provides a powerful tool for controlling the evolution of quantum systems. In this work, we investigate the potential of this approach from three perspectives. First, we consider a double-well system in the classical large-atom-number limit, deriving the exact equations of motion in the presence of feedback. Second, we consider the same system in the limit of small atom number, revealing the effect that quantum fluctuations have on the feedback scheme. Finally, we explore the behavior of modest sized Hubbard chains using exact numerics, demonstrating the near-deterministic preparation of number states, a tradeoff between local and non-local feedback for state preparation, and evidence of a feedback-driven symmetry-breaking phase transition.

1 aYoung, Jeremy, T.1 aGorshkov, Alexey, V.1 aSpielman, I., B. uhttps://arxiv.org/abs/2106.0974401499nas a2200181 4500008004100000245008200041210006900123260001400192300001100206490000600217520094800223100001801171700002501189700002101214700002101235700002401256856003701280 2021 eng d00aMachine-learning enhanced dark soliton detection in Bose-Einstein condensates0 aMachinelearning enhanced dark soliton detection in BoseEinstein  c6/17/2021 a0350200 v23 a

Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons -- appearing as local density depletions in a BEC -- using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic Bose-Einstein condensates (BECs) utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.

1 aGuo, Shangjie1 aFritsch, Amilson, R.1 aGreenberg, Craig1 aSpielman, I., B.1 aZwolak, Justyna, P. uhttps://arxiv.org/abs/2101.0540401736nas a2200133 4500008004100000245007300041210006900114260001400183520130700197100002201504700001801526700002101544856003701565 2020 eng d00aFeedback Induced Magnetic Phases in Binary Bose-Einstein Condensates0 aFeedback Induced Magnetic Phases in Binary BoseEinstein Condensa c7/14/20203 a

Weak measurement in tandem with real-time feedback control is a new route toward engineering novel non-equilibrium quantum matter. Here we develop a theoretical toolbox for quantum feedback control of multicomponent Bose-Einstein condensates (BECs) using backaction-limited weak measurements in conjunction with spatially resolved feedback. Feedback in the form of a single-particle potential can introduce effective interactions that enter into the stochastic equation governing system dynamics. The effective interactions are tunable and can be made analogous to Feshbach resonances -- spin-independent and spin-dependent -- but without changing atomic scattering parameters. Feedback cooling prevents runaway heating due to measurement backaction and we present an analytical model to explain its effectiveness. We showcase our toolbox by studying a two-component BEC using a stochastic mean-field theory, where feedback induces a phase transition between easy-axis ferromagnet and spin-disordered paramagnet phases. We present the steady-state phase diagram as a function of intrinsic and effective spin-dependent interaction strengths. Our result demonstrates that closed-loop quantum control of Bose-Einstein condensates is a powerful new tool for quantum engineering in cold-atom systems.

1 aHurst, Hilary, M.1 aGuo, Shangjie1 aSpielman, I., B. uhttps://arxiv.org/abs/2007.0726601611nas a2200205 4500008004100000245008100041210006900122260001500191490000800206520098000214100001701194700001601211700002401227700002201251700002501273700002401298700002101322700002501343856003701368 2019 eng d00aScale-Invariant Continuous Entanglement Renormalization of a Chern Insulator0 aScaleInvariant Continuous Entanglement Renormalization of a Cher c03/27/20190 v1223 a

The multi-scale entanglement renormalization ansatz (MERA) postulates the existence of quantum circuits that renormalize entanglement in real space at different length scales. Chern insulators, however, cannot have scale-invariant discrete MERA circuits with finite bond dimension. In this Letter, we show that the continuous MERA (cMERA), a modified version of MERA adapted for field theories, possesses a fixed point wavefunction with nonzero Chern number. Additionally, it is well known that reversed MERA circuits can be used to prepare quantum states efficiently in time that scales logarithmically with the size of the system. However, state preparation via MERA typically requires the advent of a full-fledged universal quantum computer. In this Letter, we demonstrate that our cMERA circuit can potentially be realized in existing analog quantum computers, i.e., an ultracold atomic Fermi gas in an optical lattice with light-induced spin-orbit coupling. 

1 aChu, Su-Kuan1 aZhu, Guanyu1 aGarrison, James, R.1 aEldredge, Zachary1 aCuriel, Ana, Valdés1 aBienias, Przemyslaw1 aSpielman, I., B.1 aGorshkov, Alexey, V. uhttps://arxiv.org/abs/1807.1148601229nas a2200169 4500008004100000245005000041210004800091260001500139490000700154520075700161100001600918700001900934700002300953700002100976700002500997856003701022 2011 eng d00aChern numbers hiding in time-of-flight images0 aChern numbers hiding in timeofflight images c2011/12/210 v843 a We present a technique for detecting topological invariants -- Chern numbers -- from time-of-flight images of ultra-cold atoms. We show that the Chern numbers of integer quantum Hall states of lattice fermions leave their fingerprints in the atoms' momentum distribution. We analytically demonstrate that the number of local maxima in the momentum distribution is equal to the Chern number in two limiting cases, for large hopping anisotropy and in the continuum limit. In addition, our numerical simulations beyond these two limits show that these local maxima persist for a range of parameters. Thus, an everyday observable in cold atom experiments can serve as a useful tool to characterize and visualize quantum states with non-trivial topology. 1 aZhao, Erhai1 aBray-Ali, Noah1 aWilliams, Carl, J.1 aSpielman, I., B.1 aSatija, Indubala, I. uhttp://arxiv.org/abs/1105.3100v3