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Related Concept Videos

Scalar and Vector Triple Products01:06

Scalar and Vector Triple Products

Two vectors can be multiplied using a scalar product or a vector product. The resultant of a scalar product is scalar, while with vector products, the resultant is a vector. These rules of the scalar or vector product between two vectors can be applied to multiple vectors to obtain meaningful combinations. The scalar triple product is the dot product of a vector with the cross product of two vectors.
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Generation and Coherent Control of Pulsed Quantum Frequency Combs
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A scalable diagonalization framework for tensor-product bitstring selected configuration interaction.

Enhua Xu1, William Dawson1, Himadri Pathak1,2

  • 1RIKEN Center for Computational Science, Kobe, Japan.

The Journal of Chemical Physics
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

We developed a scalable distributed diagonalization framework for selected configuration interaction (SCI) methods, overcoming memory bottlenecks. This tensor-product bitstring SCI (TBSCI) approach efficiently handles large electronic systems.

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Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • High-Performance Computing

Background:

  • Selected configuration interaction (SCI) methods are crucial for accurately describing strongly correlated electronic systems.
  • Existing SCI implementations face scalability limitations due to memory-intensive replication of configuration interaction (CI) vectors.

Purpose of the Study:

  • To develop a fully distributed diagonalization framework for selected determinant spaces to address SCI scalability bottlenecks.
  • To introduce and validate the tensor-product bitstring SCI (TBSCI) method.

Main Methods:

  • A novel tensor-product bitstring (TPB) representation organizes determinants using selected alpha- and beta-bitstrings.
  • An efficient TBSCI eigensolver was developed, incorporating a bitstring-based Hamiltonian evaluation and MPI communication strategies.
  • Large-scale full configuration interaction (FCI) benchmarks were used to test the distributed diagonalization.

Main Results:

  • The TBSCI eigensolver significantly reduces wall time for distributed diagonalization of extremely large determinant spaces (2.6 × 10^12 determinants).
  • Scalability was demonstrated on the Fugaku supercomputer, utilizing 54,000 nodes (>2.5 million cores).
  • The TPB representation shows intrinsic compactness, enabling wavefunctions close to the FCI limit with a reduced number of determinants.

Conclusions:

  • TBSCI establishes a new scalable SCI methodology, overcoming previous computational limitations.
  • The TPB representation offers a compact and efficient way to represent wavefunctions in large electronic systems.
  • This work paves the way for accurate quantum chemical calculations on previously intractable strongly correlated systems.