Connecting the concepts of quantum state tomography and molecular representations for machine learning

Raul Ortega Ochoa; Dominic W. Berry; Juan Bernardo Bernardo Perez Sanchez; Mohsen Bagherimehrab; Abdulrahman Aldossary; Tejs Vegge; Tonio Buonassisi; Alan Aspuru-Guzik

doi:10.1039/d5dd00484e

January 1, 2025Digital DiscoveryOpen Access

Connecting the concepts of quantum state tomography and molecular representations for machine learning

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Key Points

The central aim is to explore the intersection of quantum state tomography and molecular representations in machine learning contexts.
Analyzed the principles of quantum state tomography.
Evaluated its application in reconstructing wavefunctions.
Integrated molecular representations into machine learning frameworks.
Reconstructed quantum states effectively using complete measurements.
Enhanced molecular understanding through improved machine learning models.

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Implication

Quantum state tomography reconstructs quantum states in machine learning models, suggesting new pathways for molecular analysis.

Abstract

Quantum state tomography has been widely used to reconstruct the quantum state of a system from a set of informationally-complete measurements. Obtaining enough information about, e.g., the wavefunction of a...

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Ochoa et al. (2025) studied this question.

synapsesocial.com/papers/69994c91873532290d021130 https://doi.org/10.1039/d5dd00484e

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Connecting the concepts of quantum state tomography and molecular representations for machine learning

Key Points

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Implication

Abstract

Discussion

Cite This Study

Connecting the concepts of quantum state tomography and molecular representations for machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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