In the regime where traditional approaches to electronic structure cannot afford to achieve accurate energy differences via exhaustive wave function flexibility, rigorous approaches to balancing different states' accuracies become desirable. As a direct measure of a wave function's accuracy, the energy variance offers one route to achieving such a balance. Here, we develop and test a variance matching approach for predicting excitation energies within the context of variational Monte Carlo and selective configuration interaction. In a series of tests on small but difficult molecules, we demonstrate that the approach is effective at delivering accurate excitation energies when the wave function is far from the exhaustive flexibility limit. Results in C, where we combine this approach with variational Monte Carlo orbital optimization, are especially encouraging.
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http://dx.doi.org/10.1063/1.5008743 | DOI Listing |
Nat Comput Sci
December 2024
ByteDance Research, Fangheng Fashion Center, Beijing, P. R. China.
The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, People's Republic of China.
Quantum computing holds great potential for simulating quantum systems, such as molecular systems, due to its inherent ability to represent and manipulate quantum states. However, simulating nonunitary dynamics of open quantum systems on a quantum computer is still challenging. Here, we present a scheme denoted as VQS-QJ-LTLME, which adopts the trajectory average of quantum jump Monte Carlo wave function variational evolution to evolve the system's density matrix and local thermalizing Lindblad operators to describe the system-environment interactions.
View Article and Find Full Text PDFJ Phys Condens Matter
November 2024
Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
We revisit the equilibrium statistical mechanics of a classical fluid of point-like particles with repulsive power-law pair interactions, focusing on density and energy fluctuations at finite temperature. Such long-range interactions, decaying with inter-particle distanceas1/rsindimensions, are known to fall into two qualitatively different categories. For < ('strongly' long-range interactions) there are screening of correlations and suppression of large-wavelength density fluctuations (hyperuniformity).
View Article and Find Full Text PDFBr J Math Stat Psychol
November 2024
Faculty of Education, The University of Hong Kong, Hong Kong City, Hong Kong.
Q-matrices are crucial components of cognitive diagnosis models (CDMs), which are used to provide diagnostic information and classify examinees according to their attribute profiles. The absence of an appropriate Q-matrix that correctly reflects item-attribute relationships often limits the widespread use of CDMs. Rather than relying on expert judgment for specification and post-hoc methods for validation, there has been a notable shift towards Q-matrix estimation by adopting Bayesian methods.
View Article and Find Full Text PDFNat Commun
October 2024
Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge. Our work introduces a variational approach for fermionic time-dependent wave functions, surpassing mean-field approximations by accurately capturing many-body correlations. We employ time-dependent Jastrow factors and backflow transformations, enhanced through neural networks parameterizations.
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