Background
Morten is a computational physicist whose research spans quantum many-body theory, computational science, quantum computing, and machine learning. He has devoted his career to developing and applying advanced computational methodologies for tackling some of the most challenging problems in modern physics, with a particular emphasis on the quantum mechanics of interacting particles.
His work centres on the idea that computation is a fundamental theoretical tool, on equal footing with analysis and experiment. He has made many contributions to the development of state-of-the-art numerical algorithms, including large-scale many-body methods, quantum Monte Carlo techniques, and machine-learning–enhanced computational frameworks. His research connects nuclear physics, condensed matter theory, quantum chemistry, and quantum information science, providing unified computational approaches across these traditionally separate fields.
He has also been deeply involved in the rapidly developing interface between quantum technologies and machine learning, contributing to the advancement of quantum algorithms, quantum state engineering, and hybrid quantum–classical computational strategies. His work extends from fundamental theoretical problems — such as emergent phenomena and entanglement in strongly correlated systems — to practical algorithmic development for simulation platforms and quantum devices.
In addition to his research contributions, Morten is an active educator and mentor, known for promoting open science and open access to computational resources, developing widely used teaching materials, and contributing to community-building efforts in computational and quantum science. All course material developed by Morten is openly available from his GitHub address.
Quantum Many-Body Theory
- Emergence of correlations and entanglement in complex quantum systems
- Strongly interacting fermionic and bosonic systems
- Collective phenomena in nuclear systems, ultracold atoms, and condensed matter
Condensed Matter & Nuclear Physics
- Quantum entanglement in materials and many-body phases
- Nuclear structure and reactions
- Dense matter and astrophysical applications (neutron stars, EOS modelling)
Many-Body Computational Methods
- Full Configuration Interaction (FCI) and exact diagonalisation
- Mean-field theories: Hartree–Fock
- Green's function methods and many-body self-energy approaches
- Coupled-Cluster theory across physics and chemistry
- Many-Body Perturbation Theory (MBPT) and diagrammatic expansions
- Quantum Monte Carlo methods
Machine Learning in Physical Sciences
- Physics-informed neural networks and surrogate modelling
- ML-enhanced many-body solvers and variational approaches
- Classification and clustering of quantum phases and transitions
- Representation learning for configuration spaces and quantum states
Quantum Computing & Quantum Engineering
- Quantum algorithms for simulation of interacting systems
- Error mitigation and noise-aware state preparation
- Quantum state engineering and control
- Hybrid quantum–classical computational workflows
- Entanglement structure and resource characterisation in quantum devices
Quantum Machine Learning
- Quantum-enhanced learning architectures
- Variational quantum circuits and parametrised quantum models
- Quantum kernel methods and quantum feature spaces
Quantum Science & Technology
- Benchmarks for quantum sensors and metrological protocols
- Simulation of NISQ hardware
- Cross-disciplinary development of software tools and computational frameworks