[This corrects the article DOI: 10.1021/acsomega.3c07673.].
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http://dx.doi.org/10.1021/acsomega.3c09478 | DOI Listing |
Nat Mater
January 2025
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Physics, Wolkite University, P. O. Box: 07, Wolkite, Ethiopia.
This study uses the Quantum ESPRESSO code to introduce Hubbard correction (U) to the density functional theory (DFT) in order to examine the effects of non-metals (C, F, N, and S) doping on the structural, electronic, and optical characteristics of rutile TiO. Rutile TiO is a substance that shows promise for use in renewable energy production, including fuels and solar energy, as well as environmental cleanup. Its wide bandgap, however, restricts their uses to areas with UV light.
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 PDFSensors (Basel)
January 2025
Department of Computer-Integrated Technologies of Device Production, Faculty of Instrumentation Engineering, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Beresteiskyi Ave., 37, 03056 Kyiv, Ukraine.
This study presents a method for aligning the geometric parameters of images in multi-channel imaging systems based on the application of pre-processing methods, machine learning algorithms, and a calibration setup using an array of orderly markers at the nodes of an imaginary grid. According to the proposed method, one channel of the system is used as a reference. The images from the calibration setup in each channel determine the coordinates of the markers, and the displacements of the marker centers in the system's channels relative to the coordinates of the centers in the reference channel are then determined.
View Article and Find Full Text PDFNat Commun
January 2025
Anhui Key Laboratory of Low-Energy Quantum Materials and Devices, High Magnetic Field Laboratory, HFIPS, Chinese Academy of Sciences, Hefei, Anhui, China.
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