A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
Notes on kullback-leibler divergence and likelihood
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abstract
The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies the proximity of two probability distributions. Although difficult to understand by examining the equation, an intuition and understanding of the KL divergence arises from its intimate relationship with likelihood theory. We discuss how KL divergence arises from likelihood theory in an attempt to provide some intuition and reserve a rigorous (but rather simple) derivation for the appendix. Finally, we comment on recent applications of KL divergence in the neural coding literature and highlight its natural application.
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UNVERDICTED 2representative citing papers
A physics-informed framework reconstructs 3D atomic coordinates and dynamics of free-standing graphene from single low-dose TEM images with sub-angstrom out-of-plane accuracy.
citing papers explorer
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Quantum Machine Learning for State Tomography Using Classical Data
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
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Physics-Informed 3D Atomic Reconstruction and Dynamics of Free-Standing Graphene from Single Low-Dose TEM Images
A physics-informed framework reconstructs 3D atomic coordinates and dynamics of free-standing graphene from single low-dose TEM images with sub-angstrom out-of-plane accuracy.