Introduces effective rank κ to quantify QNN expressivity and applies reinforcement learning with a transformer agent to optimize circuit architectures for higher κ.
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An iterative semidefinite programming method maximizes quantum Fisher information over local Hamiltonians to optimize metrological performance of quantum states and solves related entanglement problems.
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Learning to Maximize Quantum Neural Network Expressivity via Effective Rank
Introduces effective rank κ to quantify QNN expressivity and applies reinforcement learning with a transformer agent to optimize circuit architectures for higher κ.
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Iterative optimization in quantum metrology and entanglement theory using semidefinite programming
An iterative semidefinite programming method maximizes quantum Fisher information over local Hamiltonians to optimize metrological performance of quantum states and solves related entanglement problems.