Introduction of paper: Resource-efficient quantum algorithm for protein folding.
This paper introduces a new method for solving protein folding problems using a new type of variational quantum computation. In this paper, we use the Count Value-at-Risk Variational Quantum Eigensolver (CVaR-VQE) method to calculate the optimal folding of angiotensin, a drug that can be represented as a chain of nine one-dimensional protein molecules. CVaR-VQE
Specifically, CVaR-VQE is used to optimize the angle between each molecule in the angiotensin, while using a genetic algorithm to select the pair of angles with the lowest energy and output the next generation from them, and so on until the population distribution converges or the number of generation changes reaches the upper limit. The method for generating angle pairs is as follows. The method to generate angle pairs is Differential Evolution (DE) method. The method of generating angle pairs is the Differential Evolution (DE) method, which replicates the action of natural selection to select angle pairs in nature. As a result, although the number of individuals going to the ground state was too small for the circuit with 128 shots, for 1024 shots, 80% of the total number of individuals reached the ground state after 80 generations. In the same way, the probability of existence in the ground state increased as the number of generation changes increased.
Although genetic algorithms are not necessary, the supplementary material also shows the results of optimization using other methods. The supplemental material also shows results using other methods of optimizing. Nevertheless, they have simplified the problem so that it can be solved on existing quantum computers, devised a new calculation method for it, and the results are good. I can't lose.