We investigate localization transitions in a one-dimensional non-Hermitian quasiperiodic chain with period-2 mosaic modulation and nonreciprocal hopping. We show that tuning the degree of non-Hermiticity can induce a rich reentrant localization behavior: As the hopping asymmetry increases, reentrant localization successively emerges, disappears, resurges, and ultimately vanishes. By mapping the non-Hermitian Hamiltonian to a Hermitian reference chain via an imaginary gauge transformation and performing a transfer-matrix analysis, we introduce an effective Lyapunov exponent gamma eff = gamma qp - alpha, where gamma qp denotes the Lyapunov exponent of the Hermitian chain and alpha characterizes the hopping asymmetry. The interplay between gamma qp and alpha reveals that reentrant localization and its resurgence arise from the competition between quasiperiodic disorder and nonHermiticity. Numerical analyses of participation ratios, single-particle spectra, and spectral winding numbers corroborate these findings.

We report a comprehensive spectroscopic investigation of p-diethynylbenzene (pDEB) using two-color resonance-enhanced multiphoton ionization (REMPI) and mass-analyzed threshold ionization (MATI) spectroscopy, complemented by density functional theory (DFT) calculations. The S1 ← S0 electronic origin is observed at 34,255 ± 2 cm-1. The adiabatic ionization energy (IE) is determined to be 69,095 ± 5 cm-1 from two-color MATI spectra recorded via the S1 origin. Notably, a narrow peak is observed at 32 cm-1 above the IE in the two-color MATI spectrum, which is assigned to a one-color, two-photon accidental resonance arising from the near-resonant condition where the S1 ← S0 transition energy (34,255 cm-1) is close to half of the IE (69,099/2 = 34,549.5 cm-1). This observation is consistent with similar reports for p-chlorofluorobenzene and p-difluorobenzene. Franck-Condon simulations show good agreement with the experimental spectra. The present results provide a spectroscopic basis for identifying pDEB and distinguishing it from its ortho and meta isomers.

The Rydberg atomic receiver (RAR), characterized by its broad bandwidth, high sensitivity, traceability, and all-optical readout, has substantial potential in wireless communication. We implement the reception of amplitude-phase shift keying (APSK) modulated microwave signals using the RAR, where its self-demodulation capability, based on electromagnetically induced transparency (EIT) and Autler-Townes (AT) splitting, preserves amplitude and phase information for decoding and data recovery. The relationship between amplitude and phase encoding orders in different APSK schemes and the bit error rate (BER) is explored. Relative to conventional amplitude-shift keying (ASK), APSK extends the phase dimension to increase data capacity proportional to the number of phase states. Additionally, APSK supports control of symbol durations, and the impact of varying symbol durations on data recovery is assessed through image transmission. This work utilizes the self-demodulation capabilities of the RAR to implement APSK within a quantum framework, thereby providing new insights for advancing quantum sensing and communication applications.

We report a self-supervised deep learning framework for Rydberg sensors that enables single-shot noise suppression matching the accuracy of multi-measurement averaging. The framework eliminates the need for clean reference signals (hardly required in quantum sensing) by training on two sets of noisy signals with identical statistical distributions. When evaluated on Rydberg sensing datasets, the framework outperforms wavelet transform and Kalman filtering, achieving a denoising effect equivalent to 10,000-set averaging while reducing computation time by three orders of magnitude. We further validate performance across diverse noise profiles and quantify the complexity-performance trade-off of U-Net and Transformer architectures, providing actionable guidance for optimizing deep learning-based denoising in Rydberg sensor systems.

We experimentally investigate the superfluid-Mott insulator transition in a 23Na spin-1 Bose-Einstein condensate (BEC) with approximate SU(3) spin-rotation symmetry, focusing on the role of spin configurations in shaping the critical behavior. Rabi oscillation images of sodium atoms (F = 1) among three magnetic sublevels in an optical lattice are obtained, with experimental results aligning well with theoretical predictions, indicating robust quantum coherence in the lattice. The phase transition from the superfluid to the Mott insulator is observed by varying the lattice depth. We find the critical behavior is universal for different spin states, which is attributed to the SU(3) rotation symmetry among the spin components. The experimentally proposed critical regimes are consistent with the theoretical estimation given by the Thomas-Fermi approximation and strong-coupling expansion. These findings demonstrate that the SF-MI transition exhibits relatively unchanged critical behavior across different spin states due to SU(3) symmetry, revealing the universal phase transition for different spin configurations.

How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose quantum compressed sensing (QCS), a paradigm that reframes signal acquisition as a unitary quantum evolution. By encoding high dimensional signal information into a single quantum probe state, then introducing domain-alignment evolution,a physically realizable unitary transformation that maps the sparse basis directly onto the measurement basis. QCS executes the support-set search at the quantum level without consuming measurement trials. The logarithmic penalty vanishes, compressing the required measurement number from the classical bound to M =O(K) and reducing reconstruction from ill posed optimization to linear estimation. We experimentally validate QCS using frequency and time domain sparse signals, confirming that the measurement number scales linearly with sparsity and decouples entirely from the signal dimension. Our work provides a physical pathway toward ultimate information acquisition efficiency, with broad implications for sensing, imaging, and communication.

Multi-channel fluorescence detection of alkali atoms offers a robust approach to overcome the intrinsically low transition probabilities of conventional single-wavelength spectroscopy and thus enhances the development of frequency standards. Here, we experimentally realize the multi-channel fluorescence spectroscopy of the Rb 5S1/2 – 7S1/2 monochromatic two-photon transition using a single 760 nm laser. Simultaneous fluorescence emissions at 780 nm, 741 nm, 795 nm, 728 nm, and approximately 420 nm are observed, consistent with the expected decay channels. The dependence of these fluorescence intensities on laser power, polarization, and vapor temperature is systematically investigated, revealing the evolution of atomic transitions under varying experimental conditions. Pressure broadening measurements of the Rb 5S1/2 – 7S1/2 transition yield a spectral linewidth of ∼ 1.08(2) MHz. Compared with conventional single-wavelength detection at 420 nm, multi-channel approach significantly improves the signal-to-noise ratio and provides comprehensive insights into the atomic structure. These results establish a reliable foundation for developing optical frequency standards based on the Rb 5S – 7S transition.

Detection of trace gases with high sensitivity and weak excitation power is highly desired for long-range remote sensing. Here, we report the detection of the greenhouse gas nitrous oxide (N2O) with the power of excitation light down to picowatts, by converting the mid-infrared laser to near-infrared photons through an intra-cavity-enhanced sum-frequency upconversion system. The intra-cavity-enhanced pumping power of 1064.0 nm reaches about 200.0 W, resulting in the conversion of the 4514.6 nm mid-infrared laser to 861.1 nm with an efficiency up to 73.4% under optimal conditions. The upconverted light is then detected by a single-photon avalanche detector, followed by a time-correlated single-photon counting module, which can measure the arrival time of each upconverted photon. By performing discrete Fourier transformations of the arrival time of the detected photons, the frequency spectrum can be determined. By using frequency modulation, this method can suppress background noise significantly. Consequently, the excitation power can be brought down to about 100 pW with the concentration of N2O being 10 ppm. As a demonstration of application, the presented system is also used for N2O sensing in an open-path geometry, highlighting the potential for stand-off leak detection. Our proposal offers promising applications to monitor trace gases over long distances with weak excitation powers.

Perfect laser mode is required in many research fields such as quantum computing and precision measurement. Fiber optics provides a useful tool for maintaining a high quality laser when transmitting the laser over a long distance. Fiber link between modular structures of complicated optical paths increases the effectiveness and robustness of the optical experiment. It is a key element in coupling the laser into fiber with high efficiency in fiber optics applications. However, in confined or vacuum environments, such as spacecraft cabins, manual alignment is nearly impossible, highlighting the need for automated solutions. In this study, we experimentally investigated several automatic coupling algorithms, including the iterative scanning method and three global optimization algorithms. The results show that Bayesian optimization, by exploiting multidimensional control and high-precision actuation, enabled the coupling efficiency to reach its maximum of higher than 93% within 10 s. These algorithms provide repeatable and high-precision fiber alignment solutions for all-optical experiments under different conditions.

This study addresses the challenge of complex sample preparation in coal quality analysis using combined Nearinfrared and X-ray fluorescence spectroscopy (NIRS-XRF), which traditionally requires fine grinding below 0.2 mm. An online analytical platform was developed to enable the direct measurement of 6 mm coarse coal particles, thereby eliminating the grinding process entirely. The effects of conveying speed (V) and detection height (H) on spectral quality and model performance were systematically investigated. Modeling based on Partial Least Squares Regression (PLSR) combined with Monte Carlo cross-validation demonstrated that model accuracy decreased with increasing V or H. Parameter optimization identified the ideal operational combination as V = 30 mm/s and H = 27 mm for online analysis. Under these conditions, the analysis of bituminous coal samples yielded mean absolute errors of 0.89% for ash content, 0.12% for total sulfur, and 0.30 MJ/kg for calorific value. While these errors are marginally higher than those obtained from finely ground samples (0.63%, 0.07%, and 0.23 MJ/kg, respectively), the achieved accuracy remains well within an acceptable range for industrial applications. This modest compromise in precision successfully eliminates the time-consuming and energy-intensive grinding step, significantly enhancing the continuity, efficiency, and practical feasibility of the analytical workflow for industrial deployment. Furthermore, validation with lignite and anthracite samples confirmed the method's effectiveness and broad adaptability across coal ranks. This work establishes the first quantitatively determined operational framework for grinding-free, online NIRS-XRF analysis of coarse coal and provides a practical solution for rapid, multi-property coal quality assessment in industrial settings.

Inspired by biomimetic strategies, crack-based strain-sensing technology has greatly advanced the hardware foundation of human-machine interfaces. However, conventional crack sensors face a critical trade-off among ultralow detection thresholds and broad-range operational ranges, which remains a key bottleneck limiting their overall performance. Here, drawing inspiration from the multilayered distribution of mechanoreceptors in human skin, we propose a hierarchical, multidimensional strain sensor architecture composed of vertically graphene (VG), MXene, and carbon nanotubes (CNTs). By introducing microchannels pattern in the VG layer together with a multi-step stretch-release coating strategy for the MXene and CNT layers, each material layer is structurally engineered in a targeted manner to form a stepwise conductive network. The synergistic coupling of these multilayer conductive pathways reduces the lower limit of detection 0.05 parts per thousand and expands the upper limit of detection to 80%, enabling a sensing range spanning five orders of magnitude. Beyond material engineering, a dual-mode machine learning framework, combining eXtreme Gradient Boosting (XGBoost) for static posture classification and Bidirectional Encoder Representations from Transformers (BERT) for dynamic motion recognition, achieves classification accuracies exceeding 99%. This cross-disciplinary integration of hierarchical material design and intelligent data analysis paves the way for next-generation wearable interfaces and healthmonitoring systems.

Laser chaos, a physical system capable of hosting chaotic phenomena, exhibits the characteristic irregular appearance and high sensitivity to initial conditions, both hallmarks of nonlinear dynamical systems, and has found widespread application in chaotic optical communications. Here, a scheme for generating ultra-broadband optical chaos is proposed and experimentally validated. This scheme utilizes a 1064 nm ultrafast pulsed laser to pump a photonic crystal fiber, enabling the simultaneous generation of chaotic signals with a wideband spectrum and high complexity across multiple distinct wavelength bands. The wavelength range of the generated multi-channel complex chaotic signals spans from the visible to the near-infrared range (480-1900 nm). Furthermore, each channel can be independently selected, and its spectral width is tunable, allowing for adaptation to various application requirements. Specifically, the effective bandwidth of each multi-channel chaotic signal reaches approximately 20 GHz. Notably, the cross-correlation coefficients between different channels simultaneously generated are faint. In contrast, the presence of extreme correlation peaks within the cross-correlation can effectively eliminate crosstalk between different chaotic optical channels. This proposed scheme offers a promising solution for parallel chaos generation, showcasing significant potential applications in fields such as optical frequency combs, multi-channel chaotic communication, multi-random bit generation, and parallel light detection and ranging.

Correlation imaging based on entangled light sources offers potential benefits over classical single-photon imaging (SPI) in noisy environments, but also faces challenges such as low brightness, long integration times, and difficulty imaging biological organisms. Here, we propose noise-resistant biologically correlated biphoton imaging (CPI) based on a super-bunching light source, addressing the challenge of severe signal-to-noise ratio degradation in conventional SPI schemes. Our CPI, based on a super-bunching light source with high brightness, broadband spectrum (400-1100 nm), and strong quantum correlation (g2(0) ∼ 250), delivers high-performance, noise-resistant operation by extracting correlated photon pairs between the reference and signal paths. Experimental results show that under extreme noise conditions (noise-to-signal ratio up to 600), the structural profile in conventional SPI is entirely obscured by noise. In contrast, CPI still clearly resolves the target structure. Quantitative analysis further reveals that, as noise intensity increases, the imaging visibility of CPI can be enhanced by more than two orders of magnitude compared to SPI schemes. This study confirms the exceptional robustness and practical utility of super-bunching light-based correlated imaging in high-noise environments, offering an effective technical approach for high-quality biological observation in complex optical conditions.

ABSTRACT Quantum‐correlated techniques enable high‐contrast and super‐resolution imaging through the feature of correlated photons. The superbunching effect with a second‐order correlation larger than 2, i.e., g (2) (0) > 2, demonstrates the existence of strong correlations between photons, which can enable stray light resistance and achieve high‐contrast imaging. Here, we present a scheme for quantum correlated imaging using single colloidal quantum dots (CQDs) with strong superbunching emission. The optimized CdSe/ZnS CQDs with g (2) (0) up to 69 and 20 under continuous‐wave and pulsed laser excitation have been prepared. We achieved noise‐resistant correlated biphoton imaging (CPI) based on single CQDs with noise intensity 104 times stronger than their photoluminescence. By modulating photoluminescence intensity, the Fourier‐domain CPI was determined with reasonably good contrast, despite noise 75 600 times stronger than biphoton counts. Our proposal may enable laboratory‐based quantum imaging to be applied to real‐world applications with the desired suppression of intense stray light.

The applicability of conventional metal oxide semiconductor (MOS) gas sensors in wearable devices is limited by their rigid substrates and extra heat loss. In this study, we developed a wearable MOS gas sensor using flexible hollow glass microfiber as the heating and synthesizing substrate. SnO2 was deposited in situ on the hollow glass microfiber surface through atomic layer deposition. Subsequently, after post annealing and applying a breathable composite protective layer, the developed O2 sensor was integrated into clothing to demonstrate its wearability. At an optimal operating temperature of 280 °C, the sensor achieved responses of 35.4% and 33.2% to 10% O2 before and after bending, while consuming only 285.1 mW of power.

InP/ZnSe/ZnS quantum dots (QDs) are promising candidates for advancing optoelectronic devices. However, their applications are limited by their low emission efficiency caused by photo-oxidation. In this study, we investigate the impact of photo-oxidation on the emission of InP/ZnSe/ZnS QDs at the ensemble and single-particle levels. Transient absorption spectroscopy reveals that photo-oxidation-induced ultrafast exciton trapping exhibits complex, multitime scale decay dynamics ranging from sub-nanoseconds to picoseconds, indicating that photo-oxidation-induced surface defects form high-density trap states with a broad, continuous energetic distribution. Single-QD spectroscopy shows that these trap states act as multiple nonradiative recombination centers that trigger band-edge carrier blinking. The extensive formation of photo-oxidation-induced defects results in photoluminescence (PL) quenching of single QDs. Monte Carlo simulations reproduce ultrafast exciton trapping-induced PL blinking and quenching and quantify the nonradiative recombination rates involved in these processes. These findings provide new insights into the photodegradation of InP QD materials and devices due to photo-oxidation and contribute to the design of novel antioxidant materials.

In recent years, wireless sensing has garnered increasing attention from both industry and academia. Despite its promise, it still faces several fundamental challenges that are difficult to overcome, including hardware thermal noise limitation, antenna size constraint, and limited frequency band resources. At the same time, rapid advancements in quantum technology are driving continuous innovations. By thoroughly understanding the principles of both wireless sensing and quantum physics, we propose a novel paradigm: quantum wireless sensing. Although quantum wireless sensing originates from quantum measurement, its focus has shifted toward practical wireless sensing tasks. This evolution calls for a reassessment of parameter estimation, grounded in the fundamental principles of quantum physics. We believe the unique advantages of quantum receivers - specifically, their high sensitivity and wide frequency range - can unlock powerful quantum sensing capabilities across various domains, including terrestrial, aerospace, and maritime applications. This article articulates our vision for this emerging field, highlighting recent advancements in quantum wireless sensing and exploring promising directions for future research.

Communication technologies advance toward secure communication, with frequency hopping communication (FH-C) widely used. However, traditional FH-C is limited by slow hopping speed, narrow bandwidth, and a few hopping points. Rydberg atoms offer a solution but face challenges like poor non-resonant microwave sensitivity and local oscillator synchronization limits. Here, we propose a frequency-hopping receiver based on atomic heterodyne detection that employs high orbital angular momentum (high-OAM) Rydberg states for resonant detection of rapidly hopping radio-frequency signals across multiple, widely separated frequency bands. The high-OAM Rydberg atoms (l=3,4,5) resonantly detect 315 MHz, 1.7 GHz, and 29.74 GHz RF hopping signals (spanning 6 octaves). With heterodyne detection and a continuous excitation scheme, the system eliminates spectral establishment time, achieving 60 khops/s without transmitter-receiver synchronization, and uses frequency combs to extend hopping points to 5 MHz around resonant frequencies. These advances demonstrate the potential of Rydberg-atom-based receivers for high-speed and wide-band frequency-hopping communication.

Conductive MXene-based fibers hold great promise in the field of flexible wearable electromagnetic interference (EMI) shielding, but their poor spinnability, disordered layer stacking, and weak interfacial interactions severely limit their performance. To overcome these challenges, we develop a synergistic material-structure strategy to fabricate coaxial chitosan@MXene/graphene oxide (GO) composite fibers with chitosan (CS) as the outer shell. The introduction of GO significantly improves the dispersibility and interfacial compatibility of MXene, thereby resolving its inherent poor spinnability. The outer CS layer not only serves as a mechanical support layer but also forms an antioxidant protective shell, effectively inhibiting the degradation of MXene in aqueous and oxygenated environments. Furthermore, we propose a facile post-processing immersion-drying strategy, the immersion process induces slip and rearrangement of MXene and GO layers. Subsequent drying and shrinkage processes further compact the layers, thereby "locking" a tightly ordered conductive structure to yield CS@Mx-G-1 fibers. In this strategy, the fiber conductivity increases from 6.56 & times; 103 S/m to 1.55 & times; 104 S/m, while the electromagnetic interference shielding effectiveness enhances from 30 dB to 45 dB. The fibers maintain stable conductivity and flexibility after two immersion cycles. This approach provides a novel strategy for constructing high-performance flexible EMI shielding fibers.

Time reflection and refraction are among the most canonical phenomena emerging from the burgeoning field of time-varying materials. However, their connection with topology remains largely unexplored. Here, we experimentally report that temporal scattering processes exhibit two distinct topological characteristics: eigenstate topological braiding and dynamical topological phase transition. By developing approaches to implement Schrödinger dynamics and engineer temporal boundaries, various temporal scattering processes are implemented in circuit metamaterials. Through measurements of scattering coefficients, we observe diverse topological braiding and linking structures, whose linking numbers are governed by the difference in topological winding numbers before and after the temporal boundary. Furthermore, we reveal that these processes exhibit dynamical critical behavior and host dynamical topological phase transitions, evidenced by quantized jumps at critical time points in the measured dynamical topological invariants. Our study demonstrates fundamental links between temporal scattering and braiding and dynamical topology, opening avenues for exploring the topological aspects of time-varying and nonequilibrium phases of matter. Emulating Schrodinger-equation-governed temporal dynamics in metamaterials is of strong interest. Here, the authors demonstrate that temporal scattering processes exhibit eigenstate topological braiding and dynamical topological phase transitions in circuit metamaterials.