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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Machine Learning the Quantum Mechanical Wave Function.

Mandira Dey¹, Debashree Ghosh¹

¹School of Chemical Sciences, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700032, India.

The Journal of Physical Chemistry. A

|October 31, 2023

Summary

Machine learning and artificial intelligence are advancing computational chemistry. These methods are key to solving complex quantum many-body problems and developing optimal wave function ansatz for strongly correlated systems.

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Area of Science:

Computational Chemistry
Quantum Many-Body Physics
Machine Learning

Background:

Strongly correlated systems pose significant challenges in computational chemistry.
Traditional multireference methods have been developed to address these complex systems.
The advent of machine learning and artificial intelligence offers new avenues for quantum mechanical calculations.

Purpose of the Study:

To review the milestones in applying machine learning to the quantum many-body problem.
To explore how artificial intelligence influences the development of wave function ansatz.
To highlight the impact of machine learning on computational chemistry challenges.

Main Methods:

Literature review of machine learning applications in quantum chemistry.
Analysis of various machine learning approaches for solving the quantum many-body problem.
Discussion of advancements in wave function ansatz optimization using AI.

Main Results:

Machine learning methods have shown promise in tackling strongly correlated systems.
Various machine learning techniques have been successfully integrated into quantum chemistry.
Significant progress has been made in developing optimal wave function ansatz through AI.

Conclusions:

Machine learning represents a paradigm shift in addressing challenging quantum many-body problems.
The integration of AI is crucial for future advancements in computational chemistry.
Continued research in machine learning will likely yield more efficient and accurate solutions for strongly correlated systems.