Provably superior accuracy in quantum stochastic modeling

Provably superior accuracy in quantum stochastic modeling

In the design of stochastic models, there is a constant trade-off between model complexity and accuracy. Here we prove that quantum models enable a more favorable trade-off. We present a technique for identifying fundamental upper bounds on the predictive accuracy of dimensionality-constrained class...

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Bibliographic Details
Main Authors: Yang, Chengran, Garner, Andrew J. P., Liu, Feiyang, Tischler, Nora, Thompson, Jayne, Yung, Man-Hong, Gu, Mile, Dahlsten, Oscar
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2023
Subjects:
Science::Physics
Stochastic-Modeling
Modeling Complexity
Online Access:https://hdl.handle.net/10356/171616
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Institution: Nanyang Technological University
Language: English
Description
Summary:In the design of stochastic models, there is a constant trade-off between model complexity and accuracy. Here we prove that quantum models enable a more favorable trade-off. We present a technique for identifying fundamental upper bounds on the predictive accuracy of dimensionality-constrained classical models. We identify quantum models that surpass this bound by creating an algorithm that learns quantum models given time-series data. We demonstrate that this quantum accuracy advantage is attainable in a present-day noisy quantum device. These results illustrate the immediate relevance of quantum technologies to time-series analysis and offer an instance where their resulting accuracy advantage can be provably established.

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