The large Hadron Collider was re-launched in the spring of 2022 after three years of maintenance and modernization. Immediately after launch, scientists announced a record of energy achieved at the BAC. To take full advantage of the significant increase in the data flow, researchers are using quantum machine learning for the first time to analyze jets.
Quantum machine learning techniques have already been used in particle physics for event classification and particle track reconstruction, but the team used them for the first time to identify the charge of the adronous jet, for which scientists have developed a variable quantum classification based on two different quantum diagrams.
The physicists used a quantum simulation to compare the efficiency of the new method with the deep neural networks currently in use, and it turns out that the quantum pattern is a little smaller than productivity, but the difference is not great.
In doing so, a new method using quantum networks achieves optimal productivity with fewer events. This will help reduce the use of resources to process huge data flows from BAC, while using a large number of functions, deep machine learning still exceeds quantum algorithms. Scientists believe that this will change when more productive quantum equipment becomes available.
Researchers have also found that quantum algorithms allow you to study the correlations between functions. This is to extract information about the correlations of the elements of the jet. This means that quantum analysis will improve the identification of the aroma of the aqueous jet.
The use of quantum machine learning is still in its infancy, says the authors of the work. As physicists gain experience with quantum computing, radical improvements in hardware and computing technologies are to be expected.