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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Fermionic Orbital Optimization in Tensor Network States.

C Krumnow¹, L Veis^2,3, Ö Legeza²

¹Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany.

Physical Review Letters

|December 3, 2016

Summary

This summary is machine-generated.

Tensor network states, particularly matrix-product states, are advanced for simulating quantum systems. This study introduces a novel method optimizing fermionic orbitals for tensor network simulations in quantum chemistry.

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Area of Science:

Computational Physics
Quantum Chemistry
Condensed Matter Physics

Background:

Tensor network states, especially matrix-product states (MPS), are effective for simulating strongly correlated spin models.
Recent applications extend MPS to interacting fermionic systems in quantum chemistry, presenting challenges with orbital choice.

Purpose of the Study:

To address the challenge of selecting optimal fermionic orbitals in tensor network simulations.
To develop a generalizable method for basis optimization within tensor network approaches.

Main Methods:

A novel algorithm intertwining matrix product state optimization with fermionic Gaussian mode transformations.
Generalization of basis changes, analogous to the Hartree-Fock method, for MPS.

Main Results:

The proposed method provides a systematic way to optimize fermionic orbitals for MPS simulations.
It offers a 'black box' tool for enhancing the efficiency and accuracy of tensor network methods.

Conclusions:

The developed algorithm effectively tackles the orbital choice problem in fermionic tensor network simulations.
This work advances the application of tensor network states in quantum chemistry and related fields.