AI Article Synopsis
- Concurrence is an important measure in quantum theory that quantifies the degree of entanglement between qubits, and local unitary invariants can effectively describe quantum states' properties.
- This paper proposes using local unitary invariants with advanced statistical methods (like multiple regression and neural networks) to predict concurrence in 2-qubit quantum states, which can be more efficient than traditional quantum state tomography.
- The study achieves a high prediction accuracy of 98.5% for concurrence across various two-qubit quantum states by deriving a functional formula based on the correlation of data from pure states and Werner states.
Article Abstract
Concurrence is a crucial entanglement measure in quantum theory used to describe the degree of entanglement between two or more qubits. Local unitary (LU) invariants can be employed to describe the relevant properties of quantum states. Compared to quantum state tomography, observing LU invariants can save substantial physical resources and reduce errors associated with tomography. In this paper, we use LU invariants as explanatory variables and employ methods such as multiple regression, tree models, and BP neural network models to fit the concurrence of 2-qubit quantum states. For pure states and Werner states, by analyzing the correlation between data, a functional formula for concurrence in terms of LU invariants is obtained. Additionally, for any two-qubit quantum states, the prediction accuracy for concurrence reaches 98.5%.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11592569 | PMC |
| http://dx.doi.org/10.3390/e26110917 | DOI Listing |
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