Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Journal of Computational Chemistry

Portably parallel construction of a configuration‐interaction wave function from a matrix–product state using the Charm ++ framework

Ting Wang, Yingjin Ma, Lian Qing Zhao, Jinrong Jiang

Published Sep 28, 2020• 6 citations
Publisher

Abstract

Abstract The construction of configuration‐interaction (CI) expansions from a matrix product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations in a large Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS employing the parallel object‐oriented Charm++ programming framework, upon which automatic load‐balancing and object migrating facilities can be employed. This procedure was employed in the MPS‐to‐CI utility (Moritz et al., J. Chem. Phys. 2007, 126, 224109), the sampling‐reconstructed complete active‐space algorithm (SR‐CAS, Boguslawski et al., J. Chem. Phys. 2011, 134, 224101), and the entanglement‐driven genetic algorithm (EDGA, Luo et al., J. Chem. Theory Comput. 2017, 13, 4699). It enhances productivity and allows the sampling programs to evolve to their population‐expansion versions, for example, EDGA with population expansion (PE‐EDGA). Further, examples of 1,2‐dioxetanone and firefly dioxetanone anion (FDO−) molecules demonstrated the following: (a) parallel efficiencies can be persistently improved by simply by increasing the proportions of the asynchronous executions and (b) a sampled CAS‐type CI wave function of a bi‐radical‐state FDO− molecule utilizing the full valence (30e,26o) active space can be constructed within a few hours with using thousands of cores.

DOI
10.1002/jcc.26424

Related papers

Portably parallel construction of a CI wave function from a matrix-product state using the Charm++ framework

Ting Wang, Yingjin Ma, Lian Qing Zhao +1 more

The constructions of configuration interaction (CI) expansions from a matrix-product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations when in a huge Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS using the Charm++ parallel programming framework, upon which automatic load balancing and object migration facilities can be employed. This procedure was employed in the MPS-to-CI utility (Moritz et al., J. Chem. Phys. 2007, 126, 224109), sampling-reconstructed complete active space algorithm (SR-CAS, Boguslawski et al., J. Chem. Phys. 2011, 134, 224101) and entanglement-driven genetic algorithm (EDGA, Luo et al., J. Chem. Theory Comput. 2017, 13, 4699-4710). It enhances productivity and allows the sampling programs evolve to their population-expansion versions (e.g., EDGA with population expansion [PE-EDGA]). Examples of 1,2-dioxetanone and firefly dioxetanone anion (FDO-) molecules demonstrated that 1) the procedure could be flexibly employed among various multi-core architectures; 2) the parallel efficiencies could be persistently improved simply by increasing the proportion of asynchronous executions; 3) PE-EDGA could construct a CAS-type CI wave function from a huge Hilbert space, with 0.9952 CI completeness and 96.7% correlation energy via ~1.66x10^6 configurations (only 0.0000028% of the total configurations) of a bi-radical state of FDO- molecule using the full valence active space within a few hours.

arXiv:2003.10861•Mar 24, 2020•0 citations

Efficient Reconstruction of CAS-CI-Type Wave Functions for a DMRG State Using Quantum Information Theory and a Genetic Algorithm

Zhen Luo, Yingjin Ma, Chungen Liu +1 more

We improve the methodology to construct a complete active space-configuration interaction (CAS-CI) expansion for density-matrix renormalization group (DMRG) wave functions using a matrix-product state representation, inspired by the sampling-reconstructed CAS [SR-CAS; Boguslawski , K. ; J. Chem. Phys. 2011 , 134 , 224101 ] algorithm. In our scheme, the genetic algorithm, in which the "crossover" and "mutation" processes can be optimized based on quantum information theory, is employed when reconstructing a CAS-CI-type wave function in the Hilbert space. Analysis of results for ground and excited state wave functions of conjugated molecules, transition metal compounds, and a lanthanide complex illustrate that our scheme is very efficient for searching the most important CI expansions in large active spaces.

arXiv:1704.08817•Journal of Chemical Theory and Computation•Sep 12, 2017•31 citations

Construction of CASCI-type wave functions for very large active spaces

Katharina Bogusławski, Konrad Heinrich Marti, Markus Reiher

We present a procedure to construct a configuration-interaction expansion containing arbitrary excitations from an underlying full-configuration-interaction-type wave function defined for a very large active space. Our procedure is based on the density-matrix renormalization group (DMRG) algorithm that provides the necessary information in terms of the eigenstates of the reduced density matrices to calculate the coefficient of any basis state in the many-particle Hilbert space. Since the dimension of the Hilbert space scales binomially with the size of the active space, a sophisticated Monte Carlo sampling routine is employed. This sampling algorithm can also construct such configuration-interaction-type wave functions from any other type of tensor network states. The configuration-interaction information obtained serves several purposes. It yields a qualitatively correct description of the molecule's electronic structure, it allows us to analyze DMRG wave functions converged for the same molecular system but with different parameter sets (e.g., different numbers of active-system (block) states), and it can be considered a balanced reference for the application of a subsequent standard multi-reference configuration-interaction method.

arXiv:1101.0528•The Journal of Chemical Physics•Jun 8, 2011•64 citations

Accelerating convergence in iterative solution for large‐scale complete active space self‐consistent‐field calculations

Takeshi Yanai, Yuki Kurashige, Debashree Ghosh +1 more

Abstract An algorithm that accelerates the convergence of the iterative optimization of the complete active space self‐consistent field (CASSCF) wavefunction so as to find a optimum solution in fewer macroiterations is described. The algorithm is oriented to large‐scale CASSCF problems that are to be solved with a combination of density matrix renormalization group (DMRG) method for the configuration interaction (CI) process. The algorithm is based on the alternating (or two‐step) CASSCF optimization in which the CI and molecular orbital (MO) parameters are optimized separately. Convergence ratio is improved by finding further optimized MOs from a linear extrapolation of the MO sets of the iteration history. The acceleration results in fewer diagonalizations in a total CASSCF calculation to save a considerable computational cost. The convergence performance is examined in a couple of realistic applications on SiC 3 and poly(phenyl)carbenes. For poly(phenyl)carbenes, the large‐size CASSCF calculations with CAS(30e,30o) that entails full π valence space as well as sp 2 orbital space of carbenes are performed by using the practical implementation of DMRG‐CASSCF in conjunction with the acceleration technique. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009

International Journal of Quantum Chemistry•Jan 1, 2009•106 citations

Efficient classical representation and quantum state preparation of complete active space wavefunctions

Hamza Jnane

Quantum computers promise to solve the electronic structure problem for a large class of molecules. However, the performance of relevant quantum algorithms hinges on preparing initial states with substantial overlap with the target eigenvector. For classically challenging molecules with strong electron correlation, starting from multi-reference states, such as complete active space (CAS) wavefunctions is necessary. Unfortunately, the most advanced state preparation protocols applied to such states result in a gate complexity that scales exponentially with the active space size $d$. In fact, even encoding a CAS state classically is traditionally believed to be intractable for chemically relevant systems. Here, we draw insights from the recently introduced Quantum Paldus Transform (QPT) to show that there exists an efficient classical representation of CAS states and to design a new state preparation routine outperforming previous ones. The QPT represents a transformation from the Fock basis to a friendlier symmetry-adapted basis. Our main contribution consists in showing that CAS states expanded in this basis can efficiently be represented as a matrix product state (MPS) with a bond dimension scaling as $O(d^2)$. One can then efficiently load the MPS on a quantum computer and use the inverse QPT to transform the state to the Fock basis. Moreover, our method can easily be extended to the efficient preparation of CAS states in first quantisation with similar complexity. Crucially, we demonstrate that the complexity of both state preparation protocols only grows polynomially as $O(d^3)$ , which constitutes to the best of our knowledge an exponential improvement over the state of the art.

quant-pharXiv:2606.19457•Jun 19, 2026•0 citations

Evolutionary algorithm based configuration interaction approach

Rahul Chakraborty, Paulami Ghosh, Debashree Ghosh

Abstract A new evolutionary algorithm for stochastic configuration interaction (CI) method designed as an affordable approximation to full configuration interaction (FCI) has been described here. The key components of the algorithm are initiation, propagation, and termination steps taking inspiration from the genetic algorithm. The propagation step is performed with cloning (retention of a Slater determinant without change), mutation (single excitation/de‐excitation), and crossover (exchange of α and β strings between two Slater determinants) and termination is selection of few Slater determinants based on certain fitness function (measure of importance of a determinant in the CI space) and rejection of the rest. We find that the absolute value of the CI coefficients is a suitable fitness function when combined with a fixed selection scheme. We have tested its accuracy in 1D Hubbard problem and ground state potential energy surface (PES) has also been constructed for symmetric bond breaking of water molecule, where the errors are found to be around 10 mEh with low non‐parallelity error, when retaining only a small fraction of the total number of Slater determinants in the final population. This shows that this method has the ability to capture both static and dynamic correlation. Performance and convergence properties of the algorithm are also tested for N2 triple bond breaking problem. The algorithm opens up a promising way for stochastic sampling of the important determinants in the full Hilbert space.

International Journal of Quantum Chemistry•Oct 10, 2017•18 citations
Abstract The construction of configuration‐interaction (CI) expansions from a matrix product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations in a large Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS employing the parallel object‐oriented Charm++ programming framework, upon which automatic load‐balancing and object migrating facilities can be employed. This procedure was employed in the MPS‐to‐CI utility (Moritz et al., J. Chem. Phys. 2007, 126, 224109), the sampling‐reconstructed complete active‐space algorithm (SR‐CAS, Boguslawski et al., J. Chem. Phys. 2011, 134, 224101), and the entanglement‐driven genetic algorithm (EDGA, Luo et al., J. Chem. Theory Comput. 2017, 13, 4699). It enhances productivity and allows the sampling programs to evolve to their population‐expansion versions, for example, EDGA with population expansion (PE‐EDGA). Further, examples of 1,2‐dioxetanone and firefly dioxetanone anion (FDO−) molecules demonstrated the following: (a) parallel efficiencies can be persistently improved by simply by increasing the proportions of the asynchronous executions and (b) a sampled CAS‐type CI wave function of a bi‐radical‐state FDO− molecule utilizing the full valence (30e,26o) active space can be constructed within a few hours with using thousands of cores.
The constructions of configuration interaction (CI) expansions from a matrix-product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations when in a huge Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS using the Charm++ parallel programming framework, upon which automatic load balancing and object migration facilities can be employed. This procedure was employed in the MPS-to-CI utility (Moritz et al., J. Chem. Phys. 2007, 126, 224109), sampling-reconstructed complete active space algorithm (SR-CAS, Boguslawski et al., J. Chem. Phys. 2011, 134, 224101) and entanglement-driven genetic algorithm (EDGA, Luo et al., J. Chem. Theory Comput. 2017, 13, 4699-4710). It enhances productivity and allows the sampling programs evolve to their population-expansion versions (e.g., EDGA with population expansion [PE-EDGA]). Examples of 1,2-dioxetanone and firefly dioxetanone anion (FDO-) molecules demonstrated that 1) the procedure could be flexibly employed among various multi-core architectures; 2) the parallel efficiencies could be persistently improved simply by increasing the proportion of asynchronous executions; 3) PE-EDGA could construct a CAS-type CI wave function from a huge Hilbert space, with 0.9952 CI completeness and 96.7% correlation energy via ~1.66x10^6 configurations (only 0.0000028% of the total configurations) of a bi-radical state of FDO- molecule using the full valence active space within a few hours.
We improve the methodology to construct a complete active space-configuration interaction (CAS-CI) expansion for density-matrix renormalization group (DMRG) wave functions using a matrix-product state representation, inspired by the sampling-reconstructed CAS [SR-CAS; Boguslawski , K. ; J. Chem. Phys. 2011 , 134 , 224101 ] algorithm. In our scheme, the genetic algorithm, in which the "crossover" and "mutation" processes can be optimized based on quantum information theory, is employed when reconstructing a CAS-CI-type wave function in the Hilbert space. Analysis of results for ground and excited state wave functions of conjugated molecules, transition metal compounds, and a lanthanide complex illustrate that our scheme is very efficient for searching the most important CI expansions in large active spaces.
We present a procedure to construct a configuration-interaction expansion containing arbitrary excitations from an underlying full-configuration-interaction-type wave function defined for a very large active space. Our procedure is based on the density-matrix renormalization group (DMRG) algorithm that provides the necessary information in terms of the eigenstates of the reduced density matrices to calculate the coefficient of any basis state in the many-particle Hilbert space. Since the dimension of the Hilbert space scales binomially with the size of the active space, a sophisticated Monte Carlo sampling routine is employed. This sampling algorithm can also construct such configuration-interaction-type wave functions from any other type of tensor network states. The configuration-interaction information obtained serves several purposes. It yields a qualitatively correct description of the molecule's electronic structure, it allows us to analyze DMRG wave functions converged for the same molecular system but with different parameter sets (e.g., different numbers of active-system (block) states), and it can be considered a balanced reference for the application of a subsequent standard multi-reference configuration-interaction method.
Abstract An algorithm that accelerates the convergence of the iterative optimization of the complete active space self‐consistent field (CASSCF) wavefunction so as to find a optimum solution in fewer macroiterations is described. The algorithm is oriented to large‐scale CASSCF problems that are to be solved with a combination of density matrix renormalization group (DMRG) method for the configuration interaction (CI) process. The algorithm is based on the alternating (or two‐step) CASSCF optimization in which the CI and molecular orbital (MO) parameters are optimized separately. Convergence ratio is improved by finding further optimized MOs from a linear extrapolation of the MO sets of the iteration history. The acceleration results in fewer diagonalizations in a total CASSCF calculation to save a considerable computational cost. The convergence performance is examined in a couple of realistic applications on SiC 3 and poly(phenyl)carbenes. For poly(phenyl)carbenes, the large‐size CASSCF calculations with CAS(30e,30o) that entails full π valence space as well as sp 2 orbital space of carbenes are performed by using the practical implementation of DMRG‐CASSCF in conjunction with the acceleration technique. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009
Quantum computers promise to solve the electronic structure problem for a large class of molecules. However, the performance of relevant quantum algorithms hinges on preparing initial states with substantial overlap with the target eigenvector. For classically challenging molecules with strong electron correlation, starting from multi-reference states, such as complete active space (CAS) wavefunctions is necessary. Unfortunately, the most advanced state preparation protocols applied to such states result in a gate complexity that scales exponentially with the active space size ddd. In fact, even encoding a CAS state classically is traditionally believed to be intractable for chemically relevant systems. Here, we draw insights from the recently introduced Quantum Paldus Transform (QPT) to show that there exists an efficient classical representation of CAS states and to design a new state preparation routine outperforming previous ones. The QPT represents a transformation from the Fock basis to a friendlier symmetry-adapted basis. Our main contribution consists in showing that CAS states expanded in this basis can efficiently be represented as a matrix product state (MPS) with a bond dimension scaling as O(d2)O(d^2)O(d2). One can then efficiently load the MPS on a quantum computer and use the inverse QPT to transform the state to the Fock basis. Moreover, our method can easily be extended to the efficient preparation of CAS states in first quantisation with similar complexity. Crucially, we demonstrate that the complexity of both state preparation protocols only grows polynomially as O(d3)O(d^3)O(d3) , which constitutes to the best of our knowledge an exponential improvement over the state of the art.
Abstract A new evolutionary algorithm for stochastic configuration interaction (CI) method designed as an affordable approximation to full configuration interaction (FCI) has been described here. The key components of the algorithm are initiation, propagation, and termination steps taking inspiration from the genetic algorithm. The propagation step is performed with cloning (retention of a Slater determinant without change), mutation (single excitation/de‐excitation), and crossover (exchange of α and β strings between two Slater determinants) and termination is selection of few Slater determinants based on certain fitness function (measure of importance of a determinant in the CI space) and rejection of the rest. We find that the absolute value of the CI coefficients is a suitable fitness function when combined with a fixed selection scheme. We have tested its accuracy in 1D Hubbard problem and ground state potential energy surface (PES) has also been constructed for symmetric bond breaking of water molecule, where the errors are found to be around 10 mEh with low non‐parallelity error, when retaining only a small fraction of the total number of Slater determinants in the final population. This shows that this method has the ability to capture both static and dynamic correlation. Performance and convergence properties of the algorithm are also tested for N2 triple bond breaking problem. The algorithm opens up a promising way for stochastic sampling of the important determinants in the full Hilbert space.