A CQM-based approach to solving a combinatorial problem with applications in drug design

TL;DR

This paper demonstrates how to solve a Knapsack problem using a quantum annealer with a Constrained Quadratic Model, and discusses potential applications in drug design involving complex search spaces.

Contribution

It introduces a CQM-based method for combinatorial optimization on quantum hardware and explores its application to drug molecule discovery.

Findings

Successfully solved a meal selection problem with a quantum annealer.

Provided a detailed implementation and code for the CQM approach.

Discussed potential for extending the method to drug design applications.

Abstract

The use of D-Wave's Leap Hybrid solver is demonstrated here in solving a Knapsack optimization problem: finding meal combinations from a fixed menu that fit a diner's constraints. This is done by first formulating the optimization problem as a Constrained Quadratic Model (CQM) and then submitting it to a quantum annealer. We highlight here the steps needed, as well as the implemented code, and provide solutions from a Chicken and Waffle restaurant menu. Additionally, we discuss how this model may be generalized to find optimal drug molecules within a large search space with many complex, and often contradictory, structures and property constraints.