Feedback induced magnetic phases in binary Bose-Einstein condensates

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

ON THEORETICAL ANALYSES OF QUANTUM SYSTEMS: PHYSICS AND MACHINE LEARNING

Engineered quantum systems can help us learn more about fundamental physics topics and quantum technologies with real-world applications. However, building them could involve several challenging tasks, such as designing more noise-resistant quantum components in confined space, manipulating continuously-measured quantum systems without destroying coherence, and extracting information about quantum phenomena using machine learning (ML) tools. In this dissertation, we present three examples from the three aspects of studying the dynamics and characteristics of various quantum systems. First, we examine a circuit quantum acoustodynamic system consisting of a superconducting qubit, an acoustical waveguide, and a transducer that nonlocally couples both. As the sound signals travel $10^5$ times slower than the light and the coupler dimension extends beyond a few phonon emission wavelengths, we can model the system as a non-Markovian giant atom. With an explicit result, we show that a giant atom can exhibit suppressed relaxation within a free space and an effective vacuum coupling emerges between the qubit excitation and a confined acoustical wave packet. Second, we study closed-loop controls for open quantum systems using weakly-monitored Bose-Einstein condensates (BECs) as a platform. We formulate an analytical model to describe the dynamics of backaction-limited weak measurements and temporal-spatially resolved feedback imprinting. Furthermore, we design a backaction-heating-prevention feedback protocol that stabilizes the system in quasi-equilibrium. With these results, we introduce closed-loop control as a powerful instrument for engineering open quantum systems. At last, we establish an automated framework consisting of ML and physics-informed models for solitonic feature identification from experimental BEC image data. We develop classification and object detection algorithms based on convolutional neural networks. Our framework eliminates human inspections and enables studying soliton dynamics from numerous images. Moreover, we publish a labeled dataset of soliton images and an open-source Python package for implementing our framework

Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research

We establish a dataset of over $1.6\times10^4$ 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.Comment: 16 pages, 4 figure

A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning

Abstract Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells

Artificial multi-enzyme cascades and whole-cell transformation for bioconversion of C1 compounds: Advances, challenge and perspectives

Artificial multi-enzyme cascades bear great potential for bioconversion of C1 compounds to value-added chemicals. Over the past decade, massive efforts have been devoted to constructing multi-enzyme cascades to produce glycolic acid, rare functional sugars and even starch from C1 compounds. However, in contrast to traditional fermentation utilizing C1 compounds with the expectation of competitive economic performance in future industrialization, multi-enzyme cascades systems in the proof-of-concept phase are facing the challenges of upscaling. Here, we offered an overview of the recent advances in the construction of in vitro multi-enzyme cascades and whole-cell transformation using C1 compounds as substrate. In addition, the existing challenges and possible solutions were also discussed aiming to combine the strengths of in vitro and in vivo multi-enzyme cascades systems for upscaling