We present two ideas at the intersection of chemistry and quantum computing. In the first part of this talk, we introduce fermionic neural-network states and use them as a variational ansatz to compute the electronic structure of molecules on finite bases, borrowing techniques from quantum computing. In the second part, we show how appropriate training of these networks, using single-qubit projective data, can improve the precision of molecular energy estimates on quantum computers.

Fermionic neural-network states: Applications in <i>ab initio</i> electronic structure and quantum computing

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.

Fermionic neural-network states: Applications in ab initio electronic structure and quantum computing