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Found 3 papers, 1 papers with code
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
The task of automating the search for optimal quantum circuits is known as quantum architecture search (QAS).
Curriculum reinforcement learning for quantum architecture search under hardware errors
In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study.
Enhancing variational quantum state diagonalization using reinforcement learning techniques
We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits.
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