Multitime quantum correlation functions are central objects in physical science, offering a direct link between the experimental observables and the dynamics of an underlying model. While experiments such as 2D spectroscopy and quantum control can now measure such quantities, the accurate simulation of such responses remains computationally expensive and sometimes impossible, depending on the system's complexity. A natural tool to employ is the generalized quantum master equation (GQME), which can offer computational savings by extending reference dynamics at a comparatively trivial cost. However, dynamical methods that can tackle chemical systems with atomistic resolution, such as those in the semiclassical hierarchy, often suffer from poor accuracy, limiting the credence one might lend to their results. By combining work on the accuracy-boosting formulation of semiclassical memory kernels with recent work on the multitime GQME, here we show for the first time that one can exploit a multitime semiclassical GQME to dramatically improve both the accuracy of coarse mean-field Ehrenfest dynamics and obtain orders of magnitude efficiency gains.
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http://dx.doi.org/10.1063/5.0219205 | DOI Listing |
Light Sci Appl
January 2025
National Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, 410082, Changsha, China.
Accurately and swiftly characterizing the state of polarization (SoP) of complex structured light is crucial in the realms of classical and quantum optics. Conventional strategies for detecting SoP, which typically involves a sequence of cascaded optical elements, are bulky, complex, and run counter to miniaturization and integration. While metasurface-enabled polarimetry has emerged to overcome these limitations, its functionality predominantly remains confined to identifying SoP within the standard Poincaré sphere framework.
View Article and Find Full Text PDFNat Commun
January 2025
Université de Lorraine, CNRS, Inria, LORIA, F-54000, Nancy, France.
The main obstacle to large scale quantum computing are the errors present in every physical qubit realization. Correcting these errors requires a large number of additional qubits. Two main avenues to reduce this overhead are (i) low-density parity check (LDPC) codes requiring very few additional qubits to correct errors (ii) cat qubits where bit-flip errors are exponentially suppressed by design.
View Article and Find Full Text PDFMol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
Department of Chemistry, Graduate School of Science, Tohoku University, Aramaki, Aoba-ku, Sendai 980-8578, Japan.
Molecular simulations offer valuable insights into thermosetting polymers' microstructures and interactions with small molecules, aiding in the development of advanced materials. In this study, we design two cyanate resin models featuring monomers of different sizes and employ a previously developed method to generate crosslinked structures. We then analyze their crosslinking processes and physicochemical properties.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece.
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.
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