Accelerating Nordic quantum research—from breakthrough ideas to demonstrated outcomes

A major area of focus is advancing next‑generation quantum algorithms—including hybrid quantum–classical methods—that can demonstrate meaningful value on early fault‑tolerant systems. Closely related is the development of application‑driven quantum error correction, where new decoding strategies and resource‑aware techniques help ensure that quantum solutions remain both practical and scalable. This work also aims to improve how domain problems are formulated, encoded, and mapped across the full quantum computing stack.

The programme further prioritises demonstrating effective integration of quantum computing and classical HPC. Hybrid workflows must optimally divide tasks, using HPC for pre- and post-processing, such as data reduction or error correction, while quantum systems handle exponentially hard problems like molecular simulations. Progress here is vital to transition quantum computing from theory to real-world impact, ensuring solutions are scalable and industrially applicable.

Tracking the overall progress of quantum computing systems, benchmarking remains a central tool and is especially relevant when comparing early error-correcting architectures vs. traditional NISQ-type systems. The defining metrics of circuit execution with logical vs. physical qubits, and between various error-correction modalities underpinning logical qubits, have yet to be developed.

Through these focus areas, the Flagship Quantum Research Projects encourage collaborators to explore bold ideas while grounding their efforts in meaningful, application‑driven outcomes. We invite you to join us in shaping the next generation of quantum breakthroughs.

Quantum computing can accelerate drug discovery by enabling precise simulations of molecular interactions that overwhelm classical methods. Hybrid quantum-classical approaches can be used to simulate protein folding and small-molecule interactions, reducing computational costs while improving accuracy. These advancements can make quantum simulations practical for pharmaceutical R&D, particularly in optimising drug-metabolism pathways and designing therapeutic proteins with enhanced efficacy.

Quantum simulations can transform material design by modelling atomic and electronic structures with unprecedented accuracy. Industry applications may include optimising battery electrolytes for improved charging speeds and longevity, as well as exploring high-temperature superconductors to revolutionise energy transmission. Current efforts combine quantum and classical techniques to simulate lithium diffusion in batteries and material stress responses under real-world conditions.

Quantum computing can enhance financial modelling through algorithms like Grover’s search and quantum amplitude estimation, which significantly speed up portfolio optimisation and complex derivative pricing. A key application is quantum Monte Carlo simulation, which reduces the computational time required for risk assessment and pricing of financial instruments.