期刊
PHYSICAL REVIEW LETTERS
卷 126, 期 19, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.126.190501
关键词
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资金
- U.S. Department of Energy, Office of Science, Office of High Energy Physics QuantISED program [DE-AC52-06NA25396, KA2401032]
- U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, Condensed Matter Theory Program
- Center for Nonlinear Studies
In this paper, the authors investigate the possibility of using quantum machine learning to study scrambling processes, and demonstrate the difficulty of learning unknown scrambling processes. The study shows that there are limitations in learning unitaries without prior information.
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 it is highly probable for any variational Ansatz 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.
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