Project MQV

Project description: Quantum Algorithms for Application, Cloud & Industry (QACI)

Applications investigated within the Quantum Algorithms for Application, Cloud & Industry (QACI) consortium of the MQV are ranging from optimization tasks in commercial applications, simulation of quantum systems for chemical, pharmaceutical or battery research to quantum machine learning for fraud detection. Use-cases are identified together with industry partners from different fields such as Infineon, DATEV, Airbus, BMW, or Roche, taking part as associated partners in MQV-associated project proposals.

The focus is on the implementation and evaluation of NISQ-compatible variational or kernel-based algorithms in order to identify a potential quantum advantage already on existing noisy hardware. For the underlying theory, the QACI consortium works closely together with the THEQUCO consortium.

For development tools and processes the QACI consortium faces the challenge of making quantum software development as easy as possible for non-experts, while at the same time creating high-performance implementations of quantum algorithms as well as evaluation and verification tools tailored to specific use-cases. To that end, common core methods and data structures based on tensor networks and decision diagrams are developed, that facilitate use-case specific circuit optimization, automated problem analysis and initial concepts of trusted quantum computing.

Publications

CleanQRL: Lightweight Single-File Implementations of Quantum Reinforcement Learning Algorithms
by G. Kruse et al., presentation at IEEE Quantum Week 2025

Quantum-Efficient Kernel Target Alignment
by R. Coelho et al., presentation at ICAART 2025

Benchmarking Quantum Reinforcement Learning
by G. Kruse et al., presentation at ICAART 2025

Hamiltonian-Based Quantum Reinforcement Learning for Neural Combinatorial Optimization
by G. Kruse et al., presentation at IEEE Quantum Week 2024

Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments
by G. Kruse et al., presentation at ICAART 2024