TY - JOUR T1 - Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons JF - Phys. Rev. Research Y1 - 2022 A1 - Shangjie Guo A1 - Sophia M. Koh A1 - Amilson R. Fritsch A1 - I. B. Spielman A1 - Justyna P. Zwolak AB -

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.

VL - 4 U4 - 023163 UR - https://arxiv.org/abs/2111.04881 U5 - https://doi.org/10.1103/PhysRevResearch.4.023163 ER - TY - JOUR T1 - Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research Y1 - 2022 A1 - Amilson R. Fritsch A1 - Shangjie Guo A1 - Sophia M. Koh A1 - I. B. Spielman A1 - Justyna P. Zwolak AB -

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.

UR - https://arxiv.org/abs/2205.09114 ER - TY - JOUR T1 - Feedback-stabilized dynamical steady states in the Bose-Hubbard model JF - Phys. Rev. Research Y1 - 2021 A1 - Jeremy T. Young A1 - Alexey V. Gorshkov A1 - I. B. Spielman AB -

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.

VL - 3 U4 - 043075 UR - https://arxiv.org/abs/2106.09744 CP - 4 U5 - https://doi.org/10.1103/PhysRevResearch.3.043075 ER - TY - JOUR T1 - Machine-learning enhanced dark soliton detection in Bose-Einstein condensates JF - Mach. Learn.: Sci. Technol. Y1 - 2021 A1 - Shangjie Guo A1 - Amilson R. Fritsch A1 - Craig Greenberg A1 - I. B. Spielman A1 - Justyna P. Zwolak AB -

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.

VL - 2 U4 - 035020 UR - https://arxiv.org/abs/2101.05404 U5 - https://doi.org/10.1088/2632-2153/abed1e ER - TY - JOUR T1 - Feedback Induced Magnetic Phases in Binary Bose-Einstein Condensates Y1 - 2020 A1 - Hilary M. Hurst A1 - Shangjie Guo A1 - I. B. Spielman AB -

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.

UR - https://arxiv.org/abs/2007.07266 ER - TY - JOUR T1 - Scale-Invariant Continuous Entanglement Renormalization of a Chern Insulator JF - Phys. Rev. Lett Y1 - 2019 A1 - Su-Kuan Chu A1 - Guanyu Zhu A1 - James R. Garrison A1 - Zachary Eldredge A1 - Ana Valdés Curiel A1 - Przemyslaw Bienias A1 - I. B. Spielman A1 - Alexey V. Gorshkov AB -

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. 

VL - 122 UR - https://arxiv.org/abs/1807.11486 CP - 120502 U5 - https://doi.org/10.1103/PhysRevLett.122.120502 ER - TY - JOUR T1 - Chern numbers hiding in time-of-flight images JF - Physical Review A Y1 - 2011 A1 - Erhai Zhao A1 - Noah Bray-Ali A1 - Carl J. Williams A1 - I. B. Spielman A1 - Indubala I. Satija AB - 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. VL - 84 UR - http://arxiv.org/abs/1105.3100v3 CP - 6 J1 - Phys. Rev. A U5 - 10.1103/PhysRevA.84.063629 ER -