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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. 

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. 

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. 

Abstract We develop a new heavy quark transport model, QLBT, to simulate the dynamical propagation of heavy quarks inside the quark-gluon plasma (QGP) created in relativistic heavy-ion collisions. Our QLBT model is based on the linear Boltzmann transport (LBT) model with the ideal QGP replaced by a collection of quasi-particles to account for the non-perturbative interactions among quarks and gluons of the hot QGP. The thermal masses of quasi-particles are fitted to the equation of state from lattice QCD simulations using the Bayesian statistical analysis method. Combining QLBT with our advanced hybrid fragmentation-coalescence hadronization approach, we calculate the nuclear modification factor$$R_\mathrm {AA}$$ $R_{\mathrm{AA}}$and the elliptic flow$$v_2$$ $v_2$ofDmesons at the Relativistic Heavy-Ion Collider and the Large Hadron Collider. By comparing our QLBT calculation to the experimental data on theDmeson$$R_\mathrm {AA}$$ $R_{\mathrm{AA}}$and$$v_2$$ $v_2$, we extract the heavy quark transport parameter$$\hat{q}$$ $\hat{q}$and diffusion coefficient$$D_\mathrm {s}$$ $D_s$in the temperature range of$$1-4~T_\mathrm {c}$$ $1-4T_c$, and compare them with the lattice QCD results and other phenomenological studies.