Preparation of ground states of various Hamiltonians is considered a computational challenge well-suited for both near-term noisy and future fault-tolerant quantum computers, with critical applications in quantum chemistry and material science. Two of the well-studied approaches for these tasks are adiabatic state preparation and variational quantum simulation. In this talk, we introduce the idea of combining the two approaches into a single technique. In other words, we consider the problem of finding optimal paths for an adiabatic evolution, and solve it variationally using a hybrid of quantum computation and classical optimization. In persuit of robust and sample-efficient classical engines performing this variational optimization, we view the problem as an optimal control problem and present results of solving it using reinforcement learning.

Adiabatic state preparation as an optimal control problem

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.