Machine learning phase transitions in a scalable manner

The rapid progress of quantum technologies and AI over the past few years has the potential to revolutionize many areas of physics where analytical solutions are not feasible and conventional simulation techniques fail. Condensed matter and particle physics systems hampered by the sign problem, as well as real-time evolution of large open quantum systems are some examples where the applications of quantum machine learning are particularly sought after, as research performed to date has not been able to provide a systematic solution to the sign problem on classical computers. 

As the applications of the quantum machine learning approach in quantum field theories are moving beyond the toy models, the parallelization of learning algorithms and alternative approaches to their efficient implementation gains in importance. In this talk, I will present two possible avenues to speed up the machine learning methods with applications to phase transitions classifications. After the discussion of the support vector machine learning model with a focus on its efficient parallelization, we will move the SVM to a quantum circuit and benchmark it on the spin systems in two dimensions.