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Abstract Quantum simulators based on trapped ions enable the study of spin systems and models with rich dynamical phenomena. The Su-Schrieffer-Heeger (SSH) model for fermions in one dimension is a canonical example that can support a topological insulator phase when couplings between sites are dimerized, featuring long-lived edge states. Here, we experimentally implement a spin-based variant of the SSH model using one-dimensional trapped-ion chains with tunable interaction range, realized in crystals containing up to 22 interacting spins. Using an array of individually focused laser beams, we apply site-specific, time-dependent Floquet fields to induce controlled bond dimerization. Under conditions that preserve inversion symmetry, we observe edge-state dynamics consistent with SSH-like behavior. We study the propagation and localization of spin excitations, as well as the evolution of highly excited configurations across different interaction regimes. These results demonstrate how precision Floquet engineering enables the exploration of complex spin models and dynamics, laying the groundwork for future preparation and characterization of topological and exotic phases of matter. 

Abstract We introduce a digital inline holography (DIH) method combined with deep learning (DL) for real-time detection and analysis of bacteria in liquid suspension. Specifically, we designed a prototype that integrates DIH with fluorescence imaging to efficiently capture holograms of bacteria flowing in a microfluidic channel, utilizing the fluorescent signal to manually identify ground truths for validation. We process holograms using a tailored DL framework that includes preprocessing, detection, and classification stages involving three specific DL models trained on an extensive dataset that included holograms of generic particles present in sterile liquid and five bacterial species featuring distinct morphologies, Gram stain attributes, and viability. Our approach, validated through experiments with synthetic data and sterile liquid spiked with different bacteria, accurately distinguishes between bacteria and particles, live and dead bacteria, and Gram-positive and negative bacteria of similar morphology, all while minimizing false positives. The study highlights the potential of combining DIH with DL as a transformative tool for rapid bacterial analysis in clinical and industrial settings, with potential extension to other applications including pharmaceutical screening, environmental monitoring, and disease diagnostics. 

Quantum processors use the native interactions between effective spins to simulate Hamiltonians or execute quantum gates. In most processors, the native interactions are pairwise, limiting the efficiency of controlling entanglement between many qubits. The capability of manipulating entanglement generated by higher-order interactions is a key challenge for the simulation of many Hamiltonian models appearing in various fields, including high-energy and nuclear physics, as well as quantum chemistry and error correction applications. Here we experimentally demonstrate control over a class of native interactions between trapped-ion qubits, extending conventional pairwise interactions to a higher order. By exploiting state-dependent squeezing operations, we realize and characterize high-fidelity gates and spin Hamiltonians comprising three- and four-body spin interactions. Our results demonstrate the potential of high-order spin interactions as a toolbox for quantum information applications. 

One-dimensional systems exhibiting a continuous symmetry can host quantum phases of matter with true long-range order only in the presence of sufficiently long-range interactions1. In most physical systems, however, the interactions are short-ranged, hindering the emergence of such phases in one dimension. Here we use a one-dimensional trapped-ion quantum simulator to prepare states with long-range spin order that extends over the system size of up to 23 spins and is characteristic of the continuous symmetry-breaking phase of matter2,3. Our preparation relies on simultaneous control over an array of tightly focused individual addressing laser beams, generating long-range spin–spin interactions. We also observe a disordered phase with frustrated correlations. We further study the phases at different ranges of interaction and the out-of-equilibrium response to symmetry-breaking perturbations. This work opens an avenue to study new quantum phases and out-of-equilibrium dynamics in low-dimensional systems. 

Abstract Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real‐time, in situ analysis using machine learning (ML)‐assisted digital inline holography (DIH). Our ML model uses a customized YOLOv5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of ML‐driven DIH approach as a sensitive and versatile diagnostic tool for real‐time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale.