Quantum Computing Applied to Industrial Optimization Problems

Quantum computing is an impending technology that holds a huge potential for societal and business disruption. However, realizing this potential requires the convergence of different technologies: quantum devices and quantum physics to perform calculations; digital technologies to generate, store, and process data; and operations research to support decision making with advanced analysis. NeQst aims to perform exploratory research on how one can use near-term quantum computers to solve challenging optimization problems in a business setting, exemplified by a series of high-impact problems suggested by major industries. These include hydropower scheduling, route planning for autonomous ships, financial fraud detection, portfolio management, and logistics and supply chain optimization. Several fundamental scientific challenges must be overcome to ensure quantum-enabled algorithms hold an advantage over the best classical methods. First, one must design hybrid quantum-classical algorithms that are tailored to the problem at hand and develop optimal methods to implement these on specific quantum hardware. Second, error mechanisms of the quantum hardware must be characterized and mitigated. Finally, one must develop sound methodologies for comparing quantum and classical algorithms.

Quantum computing is an impending technology with a huge potential for societal and business disruption. The main goal of NeQst is to make a significant contribution towards realizing this potential. To reach this goal we will perform exploratory research on how near-term quantum computers can help to solve high-impact optimization problems suggested by major industries: hydropower scheduling, route planning for autonomous ships, financial fraud detection, portfolio management, and logistics and supply chain optimization. Business use of quantum computing requires the convergence of different technologies; quantum technology, i.e., the use of quantum physics to perform calculations; digital technology, i.e., to generate, store, process data; and operations research, i.e., advanced analytical methods for decision making.