# Exponential Separations Between Learning With and Without Quantum Memory

@article{Chen2022ExponentialSB,
title={Exponential Separations Between Learning With and Without Quantum Memory},
author={Sitan Chen and Jordan S. Cotler and Hsin-Yuan Huang and Jerry Zheng Li},
journal={2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)},
year={2022},
pages={574-585}
}
• Published 10 November 2021
• Physics
• 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)
We study the power of quantum memory for learning properties of quantum systems and dynamics, which is of great importance in physics and chemistry. Many state-of-the-art learning algorithms require access to an additional external quantum memory. While such a quantum memory is not required a priori, in many cases, algorithms that do not utilize quantum memory require much more data than those which do. We show that this trade-off is inherent in a wide range of learning problems. Our results…
13 Citations

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