Provably superior accuracy in quantum stochastic modeling

• 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
• Institution: Nanyang Technological University

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.