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Machine learning helps construct compact quantum circuits for chemistry

Paper Authors:

Sonaldeep Halder,

Anish Dey,

Chinmay Shrikhande,

Rahul Maitra

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Key Details

Machine learning is used to expand an initial approximate wavefunction into dominant configurations

Incorporates high-rank excitations indirectly via 'scatterer' operators

Results in low-depth quantum circuits tailored to each molecule

Significantly enhances feasibility of quantum simulations

Easily integrates with error mitigation techniques

AI generated summary

Machine learning helps construct compact quantum circuits for chemistry

This paper develops a new protocol combining machine learning and quantum chemistry principles to construct compact, customizable quantum circuits to represent molecular wavefunctions. The approach uses a restricted Boltzmann machine to expand an initial approximate wavefunction into the dominant configurations. It incorporates high-rank excitations indirectly via 'scatterer' operators, resulting in very low-depth circuits tailored to each molecule.

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