Quantum teleportation transfers and processes quantum information through quantum entanglement channels. It is one of the most versatile protocols in quantum information science and leads to many remarkable applications, particularly the one-way quantum computing. Here, we show, for the first time, that the concept of teleportation can also be used to facilitate an important classical computing task, sampling random quantum circuits, which is highly relevant to prove the near-term demonstration of quantum computational supremacy. In our method, the classical computation in the physical-qubit state space is converted to simulate teleportation in logical-qubit state space, resulting in a much smaller number of qubits involved in classical computing. We tested this new method on 1D and 2D lattices up to 1000 qubits. This Letter presents a new quantum-inspired classical computing technology and is helpful to design and optimize classically hard quantum sampling experiments.
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http://dx.doi.org/10.1103/PhysRevLett.124.080502 | DOI Listing |
Chempluschem
January 2025
University of Vienna, Faculty of Chemistry, Währinger Str. 17, 1090, Vienna, AUSTRIA.
The Lewis acid-catalyzed coupling of alkenes and aldehydes presents a modern, versatile synthetic alternative to classical carbonyl addition chemistry, offering exceptional regio- and stereoselectivity. In this work, we present a comprehensive computational investigation into the reaction mechanism of this transformation. Our findings confirm the occurrence of an enantioselective trans-annular [1,5]-hydride shift step and demonstrate that the enantioselectivity of the reaction arises predominantly from steric clashes between functional groups in the cyclization step.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, Khalifa University, Abu Dhabi, UAE.
A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non-homogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN).
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Data61, CSIRO, Clayton, VIC, 3168, Australia.
The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to manage the vast amounts of data they generate. Chemiresistive sensor arrays (CSAs), a simple yet essential component in IoT systems, produce large datasets due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA), a widely used solution for dimensionality reduction, often struggles to preserve critical information in complex datasets.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Given their molecular properties and electronic structure, graphyne and graphdiyne are promising materials with numerous applications in different fields of material science. Dehydrobenzoannules (DBAs) are candidates that can serve as building blocks for synthesizing and designing new 2D carbon allotropes; however, only a few graphynes have been produced on a practical scale. Herein, we present our investigation of three DBAs, which serve as a model to understand the relationship between the structure and property, contributing to 2D carbon allotropes' rational design and synthetic effort.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.
This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed method with five classical metaheuristic algorithms and three BOA variable optimization methods.
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