Entanglement in a system is determined by factors like randomness and the coefficient of entanglement. This property is measured using Machine Learning or Deep Learning algorithms. In recent years, there have been significant advancements in understanding the entanglement of a system, which has applications across various domains. The main challenge is measuring the degree of entanglement without destroying it.
To address this challenge, a group of researchers developed multiple copies of quantum states and measured the degree of entanglement for each state. This method achieves high accuracy and a good F1 score but requires high computing power. It is called quantum tomography. To overcome the limitations of the traditional Machine Learning approach, researchers used Deep Learning neural networks and made educated guesses. They passed measurement descriptions through deep layers and used the maximum likelihood algorithm to obtain the output of quantum correlations or determined quantum correlations.
This Deep Learning approach significantly improved the precision and recall values. The researchers also developed an AI app using this approach, which was trained to study entangled quantum states based on numerical data representing the degree of entanglement. The model was trained with a large number of epochs and a high learning rate, resulting in more accurate results with each run.
The AI app model was tested using a dataset on the degree of entanglement, and the error rate decreased by 90%. The model was further tested in real-world environments, yielding similar results and scope for improvement as with simulated data. The research findings were published officially, and the error rate was reduced to some extent.
For more details, you can check out the research paper and reference article. Credit goes to the researchers involved in this project. Don’t forget to join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter to stay updated with the latest AI research news and projects.