Resonant bonding, arising from an unsaturated electronic occupation configuration, has been demonstrated as a crucial signature for intrinsically low lattice thermal conductivity (${\kappa }_l$), which offers a promising avenue for achieving high-performance thermoelectric materials. Using density-functional theory combined with Boltzmann transport equation calculations, we demonstrate the significant impact of resonant bonding on the thermal transport properties of a two-dimensional lead-tin-selenide ordered alloy, namely $({\mathrm{Pb}}_{0.5}{\mathrm{Sn}}_{0.5})\mathrm{Se}$. Our findings demonstrate that resonant bonding induces interactions that extend beyond the typical covalent bonding range, i.e., resonant interaction, as evidenced by the perturbed electron density distribution, the nonvanishing trace of interaction force constants over long distances, and the large convergent cutoff radius observed in thermal conductivity calculations. Consequently, the resonant interactions lead to enhanced phonon-scattering events and strong anharmonicity, resulting in an ultralow ${\kappa }_l$ around $1{\mathrm{Wm}}^{-1}{\mathrm{K}}^{-1}$ at 300 K. More interestingly, an excellent thermoelectric performance is observed in two-dimensional $({\mathrm{Pb}}_{0.5}{\mathrm{Sn}}_{0.5})\mathrm{Se}$, with a remarkably high ZT value around 3 at 800 K. This study highlights the underlying relation between the electronic bonding and thermal transport property, providing valuable insights for the design of high-efficiency thermoelectric materials.

The acoustic vortex (AV) offers a noncontact, label-free, and high-precision manipulation technology for particles and cells, holding promising application aspects in the fields of biology, chemistry, engineering, and medicine. However, the flexible multitarget manipulation is still constrained by the large array of transducers or the fixed structure of the holographic plate. In this study, a phase-controlled multiple off-axis focused AVs (OA-FAVs) approach with strengthened trapping capability and flexibility is developed based on a focused sector-vortex transducer array. By introducing additional phase shifts to sectorial sources based on the preset radial offsets in the focal plane, the multitarget manipulation is realized by OA-FAVs based on the multiplexing technology. It is proved that the maximum number of OA-FAVs is half of the element number of the sector-vortex array. The high-pressure annulus with a phase spiral of OA-FAV deteriorates gradually with an increase in the radial offset, and the maximum offset of OA-FAV that maintains the main characteristics of the vortex pattern is approximately 3.1 times the wavelength. The performance of the independent multitarget trapping, which is superior to traditional AVs, is demonstrated by the acoustic gradient force in both axial and radial directions. By utilizing the accurate phase control for an eight-element sector-vortex array, composite fields of OA-FAVs are measured, and the stable rotational trappings of two, three, and four polyethylene particles are conducted successfully in the experiment. The multitarget manipulation of OA-FAVs provides a new possibility for the real-time manipulation of multiple objects using a unilateral focused sector-vortex array with significantly improved efficiency and flexibility, exhibiting prosperous perspectives in particle manipulation and cell assembly as well as acoustic printing in biomedical applications.

The growing maturity of photonic integrated circuit (PIC) fabrication technology enables the high integration of an increasing number of optical components onto a single chip. With the incremental circuit complexity, the calibration of active phase shifters in a large-scale PIC becomes a crucial issue. The traditional one-by-one calibration techniques encounter significant hurdles with the propagation of calibration errors, and achieving the decoupling of all phase shifters for independent calibration is not straightforward. To address this issue, we propose a global calibration approach for large-scale PIC. Our method utilizes a custom network to simultaneously learn the nonlinear phase-current relations for all thermo-optic phase shifters on the PIC by minimizing the negative likelihood of the measurement datasets. Moreover, the reflectivities of all static beam splitter components can also be synchronizedly extracted using this calibration method. As an example, a quantum walk PIC with a circuit depth of 12 is calibrated, and a programmable discrete-time quantum walk is experimentally demonstrated. These results will greatly benefit the applications of large-scale PICs in photonic quantum information processing.

The performance of superconducting radio-frequency (SRF) cavities is sometimes limited by local defects. To investigate the rf properties of these local defects, especially those that nucleate rf magnetic vortices, a near-field magnetic microwave microscope is employed. Local third-harmonic response ($P_{3\mathrm{f}}$) and its temperature dependence and rf power dependence are measured for one $\mathrm{Nb}$/$\mathrm{Cu}$ film grown by direct current magnetron sputtering (DCMS) and six $\mathrm{Nb}$/$\mathrm{Cu}$ films grown by high-power impulse magnetron sputtering (HiPIMS) with systematic variation of deposition conditions. Five out of the six HiPIMS $\mathrm{Nb}$/$\mathrm{Cu}$ films show a strong third-harmonic response that is likely coming from rf vortex nucleation due to a low-$T_{\mathrm{c}}$ surface defect with a transition temperature between 6.3 and 6.8 K, suggesting that this defect is a generic feature of air-exposed HiPIMS $\mathrm{Nb}$/$\mathrm{Cu}$ films. A phenomenological model of surface-defect grain boundaries hosting a low-$T_{\mathrm{c}}$ impurity phase is introduced and studied with time-dependent Ginzburg-Landau (TDGL) simulations of probe-sample interaction to better understand the measured third-harmonic response. The simulation results show that the third-harmonic response of rf vortex nucleation caused by surface defects exhibits the same general features as the data, including peaks in third-harmonic response with temperature, and their shift and broadening with higher microwave amplitude. We find that the parameters of the phenomenological model (the density of surface defects that nucleate rf vortices and the depth an rf vortex travels through these surface defects) vary systematically with film deposition conditions. From the point of view of these two properties, the $\mathrm{Nb}$/$\mathrm{Cu}$ film that is most effective at reducing the nucleation of rf vortices associated with surface defects can be identified.

With great interest from the quantum computing community, an immense amount of R&D effort has been invested into improving superconducting qubits. The technologies developed for the design and fabrication of these qubits can be directly applied to applications for ultralow-threshold particle detectors, e.g., low-mass dark matter and far-infrared photon sensing. We propose a novel energy-resolving sensor based on the transmon qubit architecture combined with a signal-enhancing superconducting quasiparticle amplification stage. We refer to these sensors as SQUATs: superconducting quasiparticle-amplifying transmons. We detail the operating principle and design of this new sensor and predict that, with minimal R&D effort, solid-state-based detectors patterned with these sensors can achieve sensitivity to single terahertz photons, and sensitivity to $1\mathrm{meV}$ phonons in the detector absorber substrate on the microsecond timescale.

The introduction of low-loss optical fibers probably represents the single most important advance in the growth of telecommunication systems. To meet our needs for secure communications, it is likely that our classical network will soon be operating alongside what is known as a quantum network. The latter is very sensitive to loss and thus poses constraints to the performance of current fiber components. In particular, recent quantum network prototypes underline the absence of low-loss nonreciprocal fiber-based devices. Here, we present a solution to this issue by realizing low-loss (0.81 dB), broadband (at least 50-GHz bandwidth), and high-extinction (up to 27 dB) circulators, based on Mach-Zehnder interferometers including so-called fiber null-couplers. The latter are directional couplers, whose splitting ratio can be controlled by launching acoustic waves along the coupling region. Fabricated from standard single-mode fibers and actuated electrically, these circulators can be made to fit any existing optical fiber networks and could turn out to be key for the transmission and processing of optically encoded quantum information.

Understanding nanoscale electron and phonon transport is critical for the development of next-generation semiconductor technologies, where deviations from macroscopic behaviors can either limit or enhance device performance. While transient gratings generated by the interference of visible lasers can directly excite microscopic, nonequilibrium charge and heat distributions in metals and traditional semiconductors, extending this noncontact approach to ultrawide-bandgap materials involves added complexities. To address these challenges, here we introduce a tabletop deep-ultraviolet (DUV; 6.3 eV) transient grating setup, and show that it supports sub-300 nm spatial and subpicosecond temporal resolution. As an initial demonstration, we excite and probe gigahertz surface acoustic waves in thin gold films. We then perform DUV transient grating measurements of nanoscale carrier transport in diamond and discuss the carrier concentration-dependent diffusion coefficient. This DUV transient grating capability provides a versatile, noncontact tool for investigating transport at length scales below the visible diffraction limit and in wide-bandgap materials, and bridges the gap between visible and facility-scale extreme-ultraviolet transient grating capabilities.

Optical resonances can increase the sensitivity of measurements to material perturbations and also accelerate photochemical reactions. Here, we show that these two effects can be combined multiplicatively, to enhance the detection via weak or low-concentration photochemical reactions far beyond what could previously be attained. For an optical resonance with quality factor $Q$, the sensitivity of our detection scheme is enhanced by $\sim Q^2$ (where $\sim$ denotes approximate proportionality), as demonstrated by both theoretical arguments and numerical simulations of a simple optical-grating resonance coupled with reaction-diffusion equations. Such an approach opens a door to further improvements by careful design of the resonance: even a three-parameter optimization of the grating resonance yields an additional $\approx 7$ times improvement.

Parameterized quantum circuits (PQCs) have been widely used as a machine learning model to explore the potential of achieving quantum advantages for various tasks. However, training PQCs is notoriously challenging owing to the phenomenon of plateaus and/or the existence of (exponentially) many spurious local minima. To enhance trainability, in this work we propose an efficient parameter initialization strategy with theoretical guarantees. We prove that, by reducing the initial domain of each parameter inversely proportional to the square root of the circuit depth, the magnitude of the cost gradient decays at most polynomially with respect to the qubit count and circuit depth. Our theoretical results are substantiated through numerical simulations of variational quantum eigensolver tasks. Moreover, we demonstrate that the reduced-domain initialization strategy can protect specific quantum neural networks from exponentially many spurious local minima. Our results highlight the significance of an appropriate parameter initialization strategy, offering insights to enhance the trainability and convergence of variational quantum algorithms.

Charge doping represents one of the most successful approaches to regulating the properties of materials. Conventional chemical doping inevitably involves side effects from quenched disorder and is sometimes restricted by the choice of doping elements. In contrast, electrostatic doping makes it possible to inject carriers into materials in a clean manner; however, in materials with a high background carrier concentration, the working distance of electrostatic doping is rather limited due to the extremely short screening length. In this work, based on band-alignment considerations, we craft artificial superlattices of ${\mathrm{Sr}\mathrm{Ru}\mathrm{O}}_3/{\mathrm{Nd}\mathrm{Ni}\mathrm{O}}_3$ by intercalating a single-unit-cell charge-reservoir layer of ${\mathrm{Sr}\mathrm{Ru}\mathrm{O}}_3$ into a ${\mathrm{Nd}\mathrm{Ni}\mathrm{O}}_3$ matrix at varied periodicities. The designed electron transfer from ${\mathrm{Sr}\mathrm{Ru}\mathrm{O}}_3$ to ${\mathrm{Nd}\mathrm{Ni}\mathrm{O}}_3$ with accompanying orbital reconstruction is revealed by x-ray absorption spectroscopy. Such electron doping dramatically regulates the metal-insulator and antiferromagnetic transitions of ${\mathrm{Nd}\mathrm{Ni}\mathrm{O}}_3$. Moreover, bulklike E′ antiferromagnetic order is observed in the superlattice with ${\mathrm{Nd}\mathrm{Ni}\mathrm{O}}_3$ layers down to a single unit cell, which is related to the interfacial ion exchange enhanced by the strong electron transfer in the ultrathin limit. Our work raises the prospect of customizing artificial oxide materials with efficient modulation doping, which may lead to emergent functionalities unattainable in natural crystals.

Plasmonic nanostructures provide an excellent platform for tailoring the light-matter interaction, and have found applications ranging from perfect absorption to phase modulation. However, realizing narrowband perfect plasmonic absorbers and dynamical control of light response remains challenging, primarily due to the high intrinsic losses and the limited tunability of metallic components. Here, a gain-coupled-bound state in the continuum (BIC) strategy is proposed to achieve a narrowband perfect plasmonic absorber with tailored functionality. The bandwidth can be dramatically reduced, with quadratic scalability dependence on the perturbation parameters of the quasi-BIC, essentially arising from the powerful physics of BIC in radiation engineering. Moreover, this strategy can make the reflection phase of the plasmonic absorber undergo across a full phase diagram. In particular, it displays functional phase transitions upon crossing the critical coupling or lasing during tailoring loss by gain. This phase transition is further utilized to realize a highly sensitive phase interrogation refractometric sensor. This work provides a profound physical insight into the tailoring functionalities of absorption and phase response, and may open up possibilities for trace detection in biochemical and chemical analysis.

In the noisy intermediate-scale quantum (NISQ) era, the capabilities of variational quantum algorithms are greatly constrained due to a limited number of qubits and the shallow depth of quantum circuits. We may view these variational quantum algorithms as weak learners in supervised learning. Ensemble methods are general approaches to combining weak learners to construct a strong one in machine learning. In this paper, by focusing on classification, we theoretically establish and numerically verify a learning guarantee for quantum adaptive boosting (AdaBoost). The supervised-learning risk bound describes how the prediction error of quantum AdaBoost on binary classification decreases as the number of boosting rounds and sample size increase. We further empirically demonstrate the advantages of quantum AdaBoost by focusing on a 4-class classification. The quantum AdaBoost not only outperforms several other ensemble methods, but in the presence of noise it can also surpass the ideally noiseless but unboosted primitive classifier after only a few boosting rounds. Our work indicates that in the current NISQ era, introducing appropriate ensemble methods is particularly valuable in improving the performance of quantum machine learning algorithms.

We experimentally study the nonlinear propagation of spin waves in nanoscale yttrium iron garnet waveguides, where the dispersion spectrum is engineered to enable efficient four-magnon interactions over a wide range of wavelengths. We show that, under these conditions, the initial monochromatic spin wave nonlinearly generates copropagating spin waves with well-defined, discrete frequencies. This process is characterized by a low energy threshold and can be observed in a wide range of frequencies and excitation powers. Thanks to the engineered dispersion, the process enables the generation of waves with short wavelengths that cannot be excited directly by a linear excitation mechanism. The nonlinearly generated short-wavelength spin waves continuously acquire the energy from the initial pump wave during copropagation, which results in compensation for their propagation losses over significant distances. The observed phenomena can be used to implement frequency- and wavelength-conversion operations in magnonic nanodevices and circuits.

Thermometry based on magnetic resonance has been extensively studied in the context of biomedical imaging. In our previous work, we showed that superparamagnetic iron oxide nanoparticles induce a strong temperature dependence to the spin-spin relaxation time ($T_2$) of nuclear magnetic resonance (NMR) in water because $T_2$ scales with the highly temperature-sensitive self-diffusion constant of water. In this work, in addition to the self-diffusion constant of a fluid, we utilize the temperature-dependent magnetization of 4-nm diameter superparamagnetic iron oxide nanoparticles (SPIONs) to enhance $T_2$ sensitivity $({\xi }_{T_2}^T=5.0)$ by 1.4 times over self-diffusion $({\xi }_D^T=3.5)$ alone in hexane between 248 and 333 K. We extend the application of this NMR thermometry approach to engineering systems by investigating the temperature dependence of $T_2$ in mineral oil, which exhibits a remarkably high sensitivity $({\xi }_{T_2}^T=12)$ between 273 and 353 K. NMR thermometry, however, is not generally applicable to solids. Therefore, we also evaluate the potential of electron spin resonance (ESR) thermometry with SPIONs in the temperature range of 100 to 290 K. The temperature-dependent linewidth follows a $T^{-2}$ law for 4-nm SPIONs. The linewidth at 290 K at 9.4 GHz is 11 mT. For both NMR and ESR thermometry, SPIONs with a small magnetic moment, i.e., a small volume and reduced magnetization, enhance the temperature sensitivity of magnetic resonance thermometry.

The cavity magnomechanical (CMM) system offers a new platform for studying macroscopic quantum effects. In previous studies of CMM systems, quantum effects have mainly been studied in the resolved-sideband regime, which presents challenges for experimental implementation. Here, we propose a scheme to enhance quantum effects in the CMM system by applying a Floquet field to periodically modulate the magnon mode. To verify the effectiveness of the scheme, we examine several key phenomena, including quantum entanglement, mechanical squeezing, and ground-state cooling of the mechanical mode. More importantly, with the assistance of periodic modulation, these phenomena can be observed in the unresolved-sideband regime. This relaxes the requirements for the implementation of quantum effects in experimental CMM systems. Our proposed periodic modulation mechanism for CMM systems is quite general and can also be applied to enhance and generate other quantum effects in the unresolved-sideband regime.

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

Photonics holds the physical potential for achieving both high-speed and low-consumption matrix multiplication. Nonetheless, due to the insertion loss of optical phase shifters and the loss incurred from power splitting, traditional methods are challenging to achieve high throughput, precision, and energy efficiency within a single framework. In this paper, we propose a global-power-split-tree (GPST) architecture for large-scale coherent optical matrix multiplication. The insertion loss of phase shifters decreases from O(N) to O(logN) when utilizing phase shifters in the form of split-tree configurations. The architecture enhances energy utilization from O($1/N^2$) to O(1/N) through global power allocation. Theoretical analysis and simulation show that, compared with conventional coherent optical matrix multiplication architectures, GPST can achieve near $10\times$ higher energy efficiency. By employing five different neural networks to illustrate the overall power consumption of GPST, we find that compared to conventional architectures, power consumption decreases by an order of magnitude, especially when the integration scale is large, compared to conventional architectures.