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Abstract Understanding of how particles and light interact in a liquid environment is vital for optical and biological applications. MoS₂has been shown to enhance nonlinear optical phenomena due to the presence of a direct excitonic resonance. Its use in biological applications is predicated on knowledge of how MoS₂interacts with ultrafast (< 1 ps) pulses. In this experiment, the interaction between two femtosecond pulses and MoS₂nanoparticles suspended in liquid is studied. We found that the laser pulses induce bubble formation on the surface of a nanoparticle and a nanoparticle aggregate then forms on the surface of the trapped bubble. The processes of formation of the bubble and the nanoparticle aggregation are intertwined. 

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection. 

Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite these progresses, challenges persist due to system noise, errors, and decoherence. These system noises complicate the simulation of quantum systems. The depolarization channel is a standard tool for simulating a quantum system’s noise. However, modeling such noise for practical applications is computationally expensive when we have limited hardware resources, as is the case in the NISQ era. This work proposes a modified representation for a single-qubit depolarization channel. Our modified channel uses two Kraus operators based only on X and Z Pauli matrices. Our approach reduces the computational complexity from six to four matrix multiplications per channel execution. Experiments on a Quantum Machine Learning (QML) model on the Iris dataset across various circuit depths and depolarization rates validate that our approach maintains the model’s accuracy while improving efficiency. This simplified noise model enables more scalable simulations of quantum circuits under depolarization, advancing capabilities in the NISQ era. 

TiO 2 has been extensively studied in many fields including photocatalysis, electrochemistry, optics, etc. Understanding the mechanism of the anatase–rutile phase transition (ART) process is critical for the design of TiO 2 -based high-activity photocatalysts and tuning its properties for other applications. In this work, the ART process using individual anatase micro-particles with a large percentage of (001) facets was monitored and studied. Phase concentration evolution obtained via Raman microscopy was correlated with the morphological evolution observed in scanning electron microscope (SEM) images. The ART of anatase microcrystals is dominated by surface nucleation and growth, but the ART processes of individual anatase particles are distinctive and depend on the various rutile nucleation sites. Two types of transformation pathways are observed. In one type of ART pathway, the rutile phase nucleated at a corner of an anatase microcrystal and grew in one direction along the edge of the crystal firstly followed by propagation over the rest of the microcrystal in the orthogonal direction on the surface and to the bulk of the crystal. The kinetics of the ART follows the first-order model with two distinct rate constants. The fast reaction rate is from the surface nucleation and growth, and the slow rate is from the bulk nucleation and growth. In the other type of ART pathway, multiple rutile nucleation sites formed simultaneously on different edges and corners of the microcrystal. The rutile phase spread over the whole crystal from these nucleation sites with a small contribution of bulk nucleation. Our study on the ART of individual micro-sized crystals bridges the material gap between bulk crystals and nano-sized TiO 2 particles. The anatase/rutile co-existing particle will provide a perfect platform to study the synergistic effect between the anatase phase and the rutile phase in their catalytic performances. 

Studies of optical properties of doped nanocrystals of tungsten trioxide can elucidate new information about the material. A novel molecule-enhanced photoluminescence (PL) of potassium-doped tungsten trioxide (K x WO) was explored in the presence of different gases to understand charge transfer between molecules and K x WO on the properties of the material. We performed Raman spectroscopy and PL experiments in the presence of gaseous acetone or ethanol mixed with other gases (N 2 and O 2 ). PL at 630 nm from K x WO was observed and further enhanced when the sample was continuously irradiated with a 532 nm CW laser in acetone. A mechanism of strong emission of the PL induced by the charge transfer between the acetone and the K x WO is proposed. 

The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.