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NeQst - Quantum Computing Applied to Industrial Optimization Problems

NeQst aims to research how near-term quantum computers can solve challenging optimization problems in business, such as hydropower scheduling and financial fraud detection.

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Quantum computing has great potential to contribute to solve problems in society and business, but it requires convergence of different technologies.

Overcoming scientific challenges is necessary to ensure quantum-enabled algorithms hold an advantage over classical methods, including designing hybrid algorithms, mitigating hardware errors, and developing methodologies for comparing algorithms.

Quantum computing is a promising technology with significant potential to disrupt society and businesses. However, harnessing this potential requires the integration of multiple 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 conduct exploratory research into utilizing near-term quantum computers to solve complex optimization problems in a business context. These are 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.

Key Factors

Project duration

2022 - 2026


The Research Council of Norway

Cooperation Partners

  • SINTEF Digital
  • University of Oslo
  • SINTEF Energy AS
  • Equinor ASA
  • Statkraft Energy AS
  • Kongsberg Maritime AS
  • DNB Bank ASA
  • University of Tartu, FI
  • Fraunhofer-Gesellschaft zur Föderung der angewandten Forschung e.V., DE

Project Type

Competence and collaboration project.