Frontiers of Physics最新文献

We study the local quantum Fisher information (LQFI) in the mixed-spin Heisenberg XXZ chain. Both the maximal and minimal LQFI are studied and the former is essential to determine the accuracy of the quantum parameter estimation, the latter can be well used to characterize the discord-type quantum correlations. We investigate the effects of the temperature and the anisotropy parameter on the maximal LQFI and thus on the accuracy of the parameter estimation. Then we make use of the minimal LQFI to study the discord-type correlations of different site pairs. Different dimensions of the subsystems cause different values of the minimal LQFI which reflects the asymmetry of the discord-type correlation. In addition, the site pairs at different positions of the spin chains have different minimal LQFI, which reveals the influence of the surrounding spins on the bipartite quantum correlation. Our results show that the LQFI obtained through a simple calculation process provides a convenient way to investigate the discord-type correlation in high-dimensional systems.

Recent studies have shown that the construction of nanophononic metamaterials can reduce thermal conductivity without affecting electrical properties, making them promising in many fields of application, such as energy conversion and thermal management. However, although extensive studies have been carried out on thermal conductivity reduction in nanophononic metamaterials, the local heat flux characteristic is still unclear. In this work, we construct a heat flux regulator which includes a silicon nanofilm with silicon pillars. The regulator has remarkable heat flux regulation ability, and various impacts on the regulation ability are explored. Surprisingly, even in the region without nanopillars, the local heat current is still lower than that in pristine silicon nanofilms, reduced by the neighboring nanopillars through the thermal proximity effect. We combine the analysis of the phonon participation ratio with the intensity of localized phonon modes to provide a clear explanation. Our findings not only provide insights into the mechanisms of heat flux regulation by nanophononic metamaterials, but also will open up new research directions to control local heat flux for a broad range of applications, including heat management, thermoelectric energy conversion, thermal cloak, and thermal concentrator.

Engineering phonon transport in low-dimensional materials has great significance not only for fundamental research, but also for thermal management applications of electric devices. However, due to the difficulties of micro and nano processing and characterization techniques, the work on tuning phonon transport at nanoscale are scarce. In this work, by introducing Ar⁺ plasma, we probed the phonon transport in two-dimensional (2D) layered semiconductor PdSe₂ under different defect concentrations. By using thermal bridge method, the thermal conductivity was measured to decrease by 50% after a certain Ar⁺ irradiation, which implied a possible phase transition. Moreover, Raman characterizations were performed to show that the Raman sensitive peaks of PdSe₂ was red-shifted and finally became disappeared with the increase of defect concentration. “Defect engineering” proves be a practical strategy in tuning the phonon thermal transport in low-dimensional materials, thus providing guidance for potential application in designing thermoelectric devices with various emerging materials.

A simple semi-analytical collective model that takes into account the limitations of the variation interval of the collective variable is suggested to describe the chiral dynamics in triaxial odd-odd nuclei with a fixed particle–hole configuration. The collective Hamiltonian is constructed with the potential energy obtained using the postulated ansatz for the wave function symmetric with respect to chiral transformation. By diagonalizing the collective Hamiltonian the wave functions of the lowest states are obtained and the evolution of the energy splitting of the chiral doublets in transition from chiral vibration to chiral rotation regime is demonstrated.

Exciton physics in atomically thin transition-metal dichalcogenides (TMDCs) holds paramount importance for fundamental physics research and prospective applications. However, the experimental exploration of exciton physics, including excitonic coherence dynamics, exciton many-body interactions, and their optical properties, faces challenges stemming from factors such as spatial heterogeneity and intricate many-body effects. In this perspective, we elaborate upon how optical two-dimensional coherent spectroscopy (2DCS) emerges as an effective tool to tackle the challenges, and outline potential directions for gaining deeper insights into exciton physics in forthcoming experiments with the advancements in 2DCS techniques and new materials.

Optimization problems are prevalent in various fields, and the gradient-based gradient descent algorithm is a widely adopted optimization method. However, in classical computing, computing the numerical gradient for a function with d variables necessitates at least d + 1 function evaluations, resulting in a computational complexity of O(d). As the number of variables increases, the classical gradient estimation methods require substantial resources, ultimately surpassing the capabilities of classical computers. Fortunately, leveraging the principles of superposition and entanglement in quantum mechanics, quantum computers can achieve genuine parallel computing, leading to exponential acceleration over classical algorithms in some cases. In this paper, we propose a novel quantum-based gradient calculation method that requires only a single oracle calculation to obtain the numerical gradient result for a multivariate function. The complexity of this algorithm is just O(1). Building upon this approach, we successfully implemented the quantum gradient descent algorithm and applied it to the variational quantum eigensolver (VQE), creating a pure quantum variational optimization algorithm. Compared with classical gradient-based optimization algorithm, this quantum optimization algorithm has remarkable complexity advantages, providing an efficient solution to optimization problems. The proposed quantum-based method shows promise in enhancing the performance of optimization algorithms, highlighting the potential of quantum computing in this field.

Quasi-two-dimensional (2D) Ruddlesden–Popper (RP) halide perovskites, as a kind of emerged two-dimensional layered materials, have recently achieved great attentions in lasing materials field owing to their large exciton binding energy, high emission yield, large optical gain, and wide-range tuning of optical bandgap. This review will introduce research progresses of RP halide perovskites for lasing applications in aspects of materials, photophysics, and devices with emphasis on emission and lasing properties tailored by the molecular composition and interface. The materials, structures and fabrications are introduced in the first part. Next, the optical transitions and amplified spontaneous emission properties are discussed from the aspects of electronic structure, exciton, gain dynamics, and interface tailoring. Then, the research progresses on lasing devices are summarized and several types of lasers including VCSEL, DFB lasers, microlasers, random lasers, plasmonic lasers, and polariton lasers are discussed. At last, the challenges and perspectives would be provided.

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness, this variability is valuable for safe applications in hardware, such as the true random number generator (TRNG). However, the speed of TRNG is still be further improved. Here, we propose a reliable Ag/SiN_x/n-Si volatile memristor, which exhibits a typical threshold switching device with stable repeat ability and fast switching speed. This volatile-memristor-based TRNG is combined with nonlinear feedback shift register (NFSR) to form a new type of high-speed dual output TRNG. Interestingly, the bit generation rate reaches a high speed of 112 kb/s. In addition, this new TRNG passed all 15 National Institute of Standards and Technology (NIST) randomness tests without post-processing steps, proving its performance as a hardware security application. This work shows that the SiN_x-based volatile memristor can realize TRNG and has great potential in hardware network security.

In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.