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Quantum analytic descent
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DOI：10.1103/PhysRevResearch.4.023017.png)
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Abstract
En 中文
Variational algorithms have particular relevance for near-term quantum computers but require nontrivial parameter optimizations. Here we propose analytic descent: Given that the energy landscape must have a certain simple form in the local region around any reference point, it can be efficiently approximated in its entirety by a classical model-we support these observations with rigorous, complexity-theoretic arguments. One can classically analyze this approximate function to directly jump to the (estimated) minimum before determining a more refined function, if necessary. We derive an optimal measurement strategy and generally prove that the asymptotic resource cost of a jump corresponds to only a single gradient vector evaluation.
Keywords:
SIMULATION
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