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1,262 results on '"Yang, Yiqi"'

3. Algorithm for branching and population control in correlated sampling

Author

Chen, Siyuan, Yang, Yiqi, Morales, Miguel, and Zhang, Shiwei

Subjects
Physics - Computational Physics, Condensed Matter - Materials Science, Condensed Matter - Statistical Mechanics, Condensed Matter - Strongly Correlated Electrons
Abstract

Correlated sampling has wide-ranging applications in Monte Carlo calculations. When branching random walks are involved, as commonly found in many algorithms in quantum physics and electronic structure, population control is typically not applied with correlated sampling due to technical difficulties. This hinders the stability and efficiency of correlated sampling. In this work, we study schemes for allowing birth/death in correlated sampling and propose an algorithm for population control. The algorithm can be realized in several variants depending on the application. One variant is a static method that creates a reference run and allows other correlated calculations to be added a posteriori. Another optimizes the population control for a set of correlated, concurrent runs dynamically. These approaches are tested in different applications in quantum systems, including both the Hubbard model and electronic structure calculations in real materials., Comment: 10 pages, 8 figures

Published
2023
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8. Variational Benchmarks for Quantum Many-Body Problems

Author

Wu, Dian, Rossi, Riccardo, Vicentini, Filippo, Astrakhantsev, Nikita, Becca, Federico, Cao, Xiaodong, Carrasquilla, Juan, Ferrari, Francesco, Georges, Antoine, Hibat-Allah, Mohamed, Imada, Masatoshi, Läuchli, Andreas M., Mazzola, Guglielmo, Mezzacapo, Antonio, Millis, Andrew, Moreno, Javier Robledo, Neupert, Titus, Nomura, Yusuke, Nys, Jannes, Parcollet, Olivier, Pohle, Rico, Romero, Imelda, Schmid, Michael, Silvester, J. Maxwell, Sorella, Sandro, Tocchio, Luca F., Wang, Lei, White, Steven R., Wietek, Alexander, Yang, Qi, Yang, Yiqi, Zhang, Shiwei, and Carleo, Giuseppe

Subjects
Quantum Physics, Condensed Matter - Strongly Correlated Electrons, Physics - Computational Physics
Abstract

The continued development of novel many-body approaches to ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. Here we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide the most extensive curated dataset of variational calculations of many-body quantum systems to date, identifying cases where state-of-the-art numerical approaches show limited accuracy, and novel algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods towards quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible., Comment: 25 pages, 5 figures

Published
2023

9. Solving 2D and 3D lattice models of correlated fermions -- combining matrix product states with mean field theory

Author

Bollmark, Gunnar, Köhler, Thomas, Pizzino, Lorenzo, Yang, Yiqi, Shi, Hao, Hofmann, Johannes S., Zhang, Shiwei, Giamarchi, Thierry, and Kantian, Adrian

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

Correlated electron states are at the root of many important phenomena including unconventional superconductivity (USC), where electron-pairing arises from repulsive interactions. Computing the properties of correlated electrons, such as the critical temperature $T_c$ for the onset of USC, efficiently and reliably from the microscopic physics with quantitative methods remains a major challenge for almost all models and materials. In this theoretical work we combine matrix product states (MPS) with static mean field (MF) to provide a solution to this challenge for quasi-one-dimensional (Q1D) systems: Two- and three-dimensional (2D/3D) materials comprised of weakly coupled correlated 1D fermions. This MPS+MF framework for the ground state and thermal equilibrium properties of Q1D fermions is developed and validated for attractive Hubbard systems first, and further enhanced via analytical field theory. We then deploy it to compute $T_c$ for superconductivity in 3D arrays of weakly coupled, doped and repulsive Hubbard ladders. The MPS+MF framework thus enables the reliable, quantitative and unbiased study of USC and high-$T_c$ superconductivity - and potentially many more correlated phases - in fermionic Q1D systems from microscopic parameters, in ways inaccessible to previous methods. It opens the possibility of designing deliberately optimized Q1D superconductors, from experiments in ultracold gases to synthesizing new materials.

Published
2022

31. CH02 peptide promotes ex vivo expansion of umbilical cord blood-derived CD34+ hematopoietic stem/progenitor cells

Author

Yang Yiqi, Zhang Bihui, Xie Junye, Li Jingsheng, Liu Jia, Liu Rongzhan, Zhang Linhao, Zhang Jinting, Su Zijian, Li Fu, Zhang Leisheng, Hong An, and Chen Xiaojia

Subjects
CH02 peptide, umbilical cord blood, CD34+ hematopoietic stem/progenitor cell, Fms-like tyrosine kinase receptor 3, wound healing, Biochemistry, QD415-436, Genetics, QH426-470
Abstract

Umbilical cord blood (UCB) is an advantageous source for hematopoietic stem/progenitor cell (HSPC) transplantation, yet the current strategies for large-scale and cost-effective UCB-HSPC preparation are still unavailable. To overcome these obstacles, we systematically evaluate the feasibility of our newly identified CH02 peptide for ex vivo expansion of CD34+ UCB-HSPCs. We herein report that the CH02 peptide is specifically enriched in HSPC proliferation via activating the FLT3 signaling. Notably, the CH02-based cocktails are adequate for boosting 12-fold ex vivo expansion of UCB-HSPCs. Meanwhile, CH02-preconditioned UCB-HSPCs manifest preferable efficacy upon wound healing in diabetic mice via bidirectional orchestration of proinflammatory and anti-inflammatory factors. Together, our data indicate the advantages of the CH02-based strategy for ex vivo expansion of CD34+ UCB-HSPCs, which will provide new strategies for further development of large-scale HSPC preparation for clinical purposes.

Published
2023
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46. SDCN: A Species-Disease Hybrid Convolutional Neural Network for Plant Disease Recognition

Author

Yang, Yiqi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pimenidis, Elias, editor, Angelov, Plamen, editor, Jayne, Chrisina, editor, Papaleonidas, Antonios, editor, and Aydin, Mehmet, editor

Published
2022
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