Work

Quantifying Quantum Coherence Using Machine Learning Methods

Year: 2024

Type: article

Abstract: Quantum coherence is a crucial resource in numerous quantum processing tasks. The robustness of coherence provides an operational measure of quantum coherence, which can be calculated for various states using semidefinite programming. However, this method depends on convex optimization and can be time-intensive, especially as the dimensionality of the space increases. In this study, we employ machine learning techniques to quantify quantum coherence, focusing on the robustness of coherence. By leveraging artificial neural networks, we developed and trained models for systems with different dimensionalities. Testing on data samples shows that our approach substantially reduces computation time while maintaining strong generalizability.

Source: Applied Sciences

DOI: 10.3390/app14167312

Authors: Lin Zhang, Liang Chen, Qiliang He, Yeqi Zhang

Institutions: North China Electric Power University, Guizhou Normal University

Language: English

Topic: Quantum Information and Cryptography

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities