96 research outputs found

    Restart expedites quantum walk hitting times

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    Classical first-passage times under restart are used in a wide variety of models, yet the quantum version of the problem still misses key concepts. We study the quantum hitting time with restart using a monitored quantum walk. The restart strategy eliminates the problem of dark states, i.e. cases where the particle evades detection, while maintaining the ballistic propagation which is important for fast search. We find profound effects of quantum oscillations on the restart problem, namely a type of instability of the mean detection time, and optimal restart times that form staircases, with sudden drops as the rate of sampling is modified. In the absence of restart and in the Zeno limit, the detection of the walker is not possible and we examine how restart overcomes this well-known problem, showing that the optimal restart time becomes insensitive to the sampling period.Comment: 13 pages, 11 figure

    Large fluctuations of the first detected quantum return time

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    How long does it take a quantum particle to return to its origin? As shown previously under repeated projective measurements aimed to detect the return, the closed cycle yields a geometrical phase which shows that the average first detected return time is quantized. For critical sampling times or when parameters of the Hamiltonian are tuned this winding number is modified. These discontinuous transitions exhibit gigantic fluctuations of the return time. While the general formalism of this problem was studied at length, the magnitude of the fluctuations, which is quantitatively essential, remains poorly characterized. Here, we derive explicit expressions for the variance of the return time, for quantum walks in finite Hilbert space. A classification scheme of the diverging variance is presented, for four different physical effects: the Zeno regime, when the overlap of an energy eigenstate and the detected state is small and when two or three phases of the problem merge. These scenarios present distinct physical effects which can be analyzed with the fluctuations of return times investigated here, leading to a topology-dependent time-energy uncertainty principle

    "half-electron (e/2)" -- free electron fractional charge induced by twisted light

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    Recent advances in ultrafast electron emission, microscopy, and diffraction reveal our capacity to manipulate free electrons with remarkable quantum coherence using light beams. Here, we present a framework for exploring free electron fractional charge in ultrafast electron-light interactions. An explicit Jackiw-Rebbi solution of free electron is constructed by a spatiotemporally twisted laser field, showcasing a flying topological quantum number with a fractional charge of e/2 (we call it "half-electron"), which is dispersion-free due to its topological nature. We also propose an Aharonov-Bohm interferometry for detecting these half-electrons. The half-electron is a topologically protected bound state in free-space propagation, expands its realm beyond quasiparticles with fractional charges in materials, enabling to advance our understanding of exotic quantum and topological effects of free electron wavefunction.Comment: 23 pages, 4 figures, supplementary materia

    Compressive performance of fiber reinforced polymer encased recycled concrete with nanoparticles

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    Nanomaterials have been used in improving the performance of construction materials due to their compacting micro-structure effect and accelerating cement hydration reaction. Considering the brittle characteristic of fiber reinforced polymer (termed as FRP) tube encased concrete and inferior properties of recycled concrete, nanoparticles were used in FRP tube encased recycled aggregate concrete. The axial compressive performance of FRP tube used in recycled concrete treated with nanoparticles strengthening, termed as FRP-NPRC, were investigated by axial compression experiments and theoretical analysis. Five experimental variables were considered including (1) the dosages and (2) varieties of nanoparticles (i.e. 1% and 2% of nanoSiO2, 1% and 2% of nanoCaCO3), (3) replacement ratios of recycled coarse aggregates (termed as RCAs) (0%, 50%, 70% and 100%) the RCAs were mainly produced from the waste cracked bricks, (4) the number of glass FRP (GFRP) tube layers (2, 4 and 6-layer) and (5) the mixing methods of concrete. Results indicate that the combination of FRP confinement and nanoparticle modification in recycled concrete exhibited up to 76.2% increase in compressive strength and 7.62 times ductility improvement. Furthermore, a design-oriented stress–strain model on the basis of the ultimate condition analysis were executed to evaluate the stress–strain property of this strengthened component

    An immunological electrospun scaffold for tumor cell killing and healthy tissue regeneration

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    Antibody-based cancer immune therapy has attracted lots of research interest in recent years; however, it is greatly limited by the easy distribution and burst release of antibodies. In addition, after the clearance of the tissue, healthy tissue regeneration is another challenge for cancer treatment. Herein, we have developed a specific immunological tissue engineering scaffold using the agonistic mouse anti-human CD40 antibody (CD40mAb) incorporated into poly(l-lactide) (PLLA) electrospun fibers through the dopamine (PDA) motif (PLLA-PDA-CD40mAb). CD40mAb is successfully incorporated onto the surface of the electrospun fibrous scaffold, which is proved by immunofluorescence staining, and the PLLA-PDA-CD40mAb scaffold has an anti-tumor effect by locally releasing CD40mAb. Therefore, this immunological electrospun scaffold has very good potential to be developed as a powerful tool for localized tumor treatment, and this is the first to be reported in this area.Peer reviewe

    ARTâ‹…\boldsymbol{\cdot}V: Auto-Regressive Text-to-Video Generation with Diffusion Models

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    We present ARTâ‹…\boldsymbol{\cdot}V, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ARTâ‹…\boldsymbol{\cdot}V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ARTâ‹…\boldsymbol{\cdot}V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g., composing a long video from multiple text prompts.Comment: 24 pages, 21 figures. Project page at https://warranweng.github.io/art.
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