A reward-penalty reinforcement-learning loop learns the unitary that maps the computational basis to the eigenbasis of a Hamiltonian, demonstrated on systems of up to six qubits.
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Exploring fixed points and eigenstates of quantum systems with reinforcement learning
A reward-penalty reinforcement-learning loop learns the unitary that maps the computational basis to the eigenbasis of a Hamiltonian, demonstrated on systems of up to six qubits.