Year: 2023
Type: article
Abstract: 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.
Source: Physical Review A
Institutions: Centre for Quantum Technologies, National University of Singapore, Nanyang Technological University, Austrian Academy of Sciences, Southern University of Science and Technology, Griffith University, Centre for Quantum Computation and Communication Technology, Freie Universität Berlin, Centre National de la Recherche Scientifique, UMI MajuLab, City University of Hong Kong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funders: Silicon Valley Community Foundation, Fetzer Institute, Alexander von Humboldt-Stiftung, City University of Hong Kong, Foundational Questions Institute, National Research Foundation, Agency for Science, Technology and Research, National Research Foundation Singapore, Ministry of Education - Singapore, Griffith University, National Natural Science Foundation of China, Australian Research Council
OpenAlex ID: W4385737384