Team: 27
School: Los Alamos High
Area of Science: Computational Biology
Proposal: Predicting how a biological protein will fold given its sequence, and designing protein sequences to fold in a specific way could allow us to create purpose-built drugs and powerful medicines. However, there is a huge number of possible ways a protein can fold, but only one very low-energy structure is the true solution. Because of this, traditional computer algorithms to predict folding take a lot of time to execute and may not find the lowest-energy solution. We propose quantum computing as a means of computing the lowest-energy solution faster by considering all the solutions at once and exploiting quantum mechanics to find the best structure. Quantum annealing is a type of quantum computing that has shown promise in recent years, being more feasible with current technology. Rather than using logic gates, quantum annealing can quickly find the solution to quadratic unconstrained binary optimization problems (QUBO) by assigning penalties or bonuses to qubits for resolving to zero or one. The problem of protein structure prediction and design can be approximated in a way that can be encoded as a QUBO problem, and so it can be evaluated on quantum annealing computers. In this project, we will design algorithms to set up a quantum annealer to solve a protein structure prediction problem, or design a protein for a function. We will compare this to modern methods that use classical computers, to see whether quantum computers are an effective tool for this problem.
Team Members:
Sponsoring Teacher: Nathaniel Morgan