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Effective Reconstruction of Expectation Values from Ab Initio Quantum Embedding.

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New quantum embedding methods improve calculations by better combining fragmented solutions. These advanced functionals enhance accuracy and efficiency for quantum system properties, even with smaller clusters.

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Area of Science:

  • Quantum chemistry
  • Computational physics
  • Materials science

Background:

  • Quantum embedding fragments large quantum systems into smaller, manageable cluster problems.
  • Exploiting locality is key, but recombining fragmented solutions for nonlocal properties remains a challenge.

Purpose of the Study:

  • To critically review and develop improved methods for recombining fragmented quantum solutions.
  • To compute nonlocal expectation values and total energy more accurately.
  • To enhance the efficiency and convergence of quantum embedding calculations.

Main Methods:

  • Developed alternative approaches to density matrix embedding theory's democratic partitioning.
  • Incorporated N-representability and multi-fragment contributions to alleviate locality approximations.
  • Numerically demonstrated efficiency and accuracy for molecular and solid-state systems.

Main Results:

  • Proposed functionals show improved accuracy for energetics and nonlocal two-body observables.
  • Demonstrated robust and systematic convergence with increasing cluster size.
  • Achieved desired accuracy with significantly smaller clusters compared to traditional methods.

Conclusions:

  • The introduced functionals enable reliable extraction of observables in quantum embedding.
  • These methods offer systematic convergence and improved accuracy, reducing computational cost.
  • Advanced recombination strategies are crucial for overcoming the locality approximation in quantum embedding.