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• W3090921460 abstract "Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. We prove a no-go theorem for learning an unknown scrambling process with QML, showing that any variational ansatz is highly probable to have a barren plateau landscape, i.e., cost gradients that vanish exponentially in the system size. This implies that the required resources scale exponentially even when strategies to avoid such scaling (e.g., from ansatz-based barren plateaus or No-Free-Lunch theorems) are employed. Furthermore, we numerically and analytically extend our results to approximate scramblers. Hence, our work places generic limits on the learnability of unitaries when lacking prior information." @default.
• W3090921460 created "2020-10-08" @default.
• W3090921460 creator A5041875496 @default.
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• W3090921460 date "2021-05-12" @default.
• W3090921460 modified "2023-10-07" @default.
• W3090921460 title "Barren Plateaus Preclude Learning Scramblers" @default.
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• W3090921460 doi "https://doi.org/10.1103/physrevlett.126.190501" @default.
• W3090921460 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34047576" @default.
• W3090921460 hasPublicationYear "2021" @default.
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