Abstract
In high energy physics, the term Quantum Chromodynamics (QCD) refers to the study of strong interaction. As one of the four fundamental interactions, strong interaction remains unclear to scientists in many ways, and QCD theory still has ambiguity in large number of predictions, among which the phase diagram is a significant and symbolic one. Due to its quantum nature, it has been quite hard for scientists to practically yield precise results out of the QCD theory and paint good phase diagrams in some particular parameter regions. Quantum computers are potentially considered helpful in solving this issue, but they yet still struggle to become mature.
Meanwhile, classical computation power is more than abundant in this era, with various AI techniques ready to choose from. Thus, we plan to utilize an AI model to facilitate computation of a quantum problem, with the target of a better phase diagram painting. We demonstrate our results with a toy model of QCD theory, namely the Schwinger Model, due to its resemblance with QCD theory and the less computational power required. We will show that it is possible to implicitly represent a quantum problem with classical computation resources, and that this technique can drastically save computation time in future studies.
Anyone interested is welcome to attend.