QTAIQuantum technologies
& Artificial Intelligence

Independent advice. Based in Norway.

New technology.
Clear direction.

Make informed decisions about quantum technologies and AI. Build the skills to put them to work.

QTAI connects scientific understanding with your organisation’s questions — from quantum computing and sensing to machine learning and hybrid quantum methods.

Explore our expertise Discover our courses
AT THE INTERSECTION OF QUANTUM & AI
An abstract quantum wave connects with a network of computational nodes.
01 / From complex possibilities to meaningful connections.

From understanding
to a useful next step.

Independent adviceScientific depthPractical training

01 / Our expertise

Understand the possibilities.
Find what matters to you.

Clear advice on emerging technologies, with an honest assessment of their capabilities, limitations and relevance to your work.

01A quantum state represented on a Bloch sphere, alongside a measurement signal.

Compute · Simulate · Sense

Quantum technologies

Understand quantum computing, simulation and sensing. Explore where these approaches could be relevant and what their adoption would require.

  • Technology and readiness assessments
  • Use-case exploration and feasibility
  • Research-informed technology roadmaps
Discuss quantum technologies
02A neural network transforms inputs into predictions.

Learn · Model · Predict

AI & machine learning

Connect your data and scientific knowledge to a well-defined problem. Assess suitable methods and design a practical path to testing them.

  • Problem framing and method selection
  • Scientific and physics-informed learning
  • Validation and reproducible workflows
Discuss AI & machine learning
03A parameterised quantum circuit with measurement and a classical feedback loop.

Quantum + Classical

Quantum machine learning

Explore the intersection of quantum algorithms and machine learning. Evaluate hybrid methods against appropriate classical alternatives.

  • Hybrid algorithms and model design
  • Simulation and small-scale prototypes
  • Critical assessment of potential benefits
Discuss quantum machine learning

Start with a question. We can help define the scope before choosing a technology.

02 / How we work

Scientific rigour.
Practical next steps.

A focused engagement can begin with a technology briefing, develop into a feasibility study, or help your team build capability through training.

01

Understand

Clarify your goals, current capabilities and the question you need to answer.

A well-defined brief
02

Assess

Examine the relevant methods, evidence, resource needs and technical limits.

An informed assessment
03

Test

When useful, scope a small study or prototype with clear evaluation criteria.

Evidence for a decision
04

Equip

Translate the findings into recommendations and focused training for your team.

Skills and a way forward

The scope, deliverables and schedule are agreed around your needs.

03 / Intensive courses

Build understanding.
Develop capability.

Short, focused courses informed by extensive university teaching and open educational material. Adapted to your team’s background and goals.

Suggested formats · Tailored on request

Showing all 5 courses

01A clear introduction to quantum technologiesComputing, simulation and sensing · No specialist background needed1 day

Build a shared vocabulary for quantum technologies and an informed view of their opportunities and constraints.

  • Quantum states, measurement and entanglement
  • Computing and sensing: principles and use cases
  • Readiness, limitations and questions to ask suppliers
02Machine learning for scientific & technical workFrom problem definition to evaluation · Practical Python exercises2 days

Work through a practical modelling workflow, from preparing data to comparing models and interpreting results.

  • Regression, classification and neural networks
  • Generalisation, uncertainty and model validation
  • Reproducible experiments and appropriate baselines
03Quantum computing & hybrid algorithmsCircuits, simulation and variational methods · Hands-on learning3 days

Connect quantum computing fundamentals to implementable circuit models and hybrid classical–quantum workflows.

  • Qubits, gates, measurement and circuit simulation
  • Variational algorithms and optimisation
  • Noise, finite sampling and classical comparisons
04Exploring quantum machine learningQuantum models, classical learning and critical benchmarking2 days

Explore how quantum models can be used in learning tasks and how to assess their performance with appropriate care.

  • Data encoding and parameterised quantum circuits
  • Hybrid training and optimisation challenges
  • Benchmark design and comparison with classical models
05Physics-informed learning & surrogate modelsConnecting scientific models with machine learning2 days

Examine ways to incorporate physical knowledge in learning and use trained models to support computational studies.

  • Scientific constraints and physics-informed models
  • Surrogate modelling for computational problems
  • Validation, uncertainty and limits of applicability

Need a different starting point? A focused briefing or a course built around your own questions may be the right fit.

Discuss a tailored course
Morten Hjorth-Jensen in a mountain landscape.
Morten Hjorth-Jensen Researcher · Educator · Adviser

04 / The expertise behind QTAI

A scientific foundation.
A teaching mindset.

QTAI brings together Morten Hjorth-Jensen’s experience in computational physics, quantum theory and machine learning.

Morten is a Professor of Physics at the University of Oslo. His research spans quantum many-body theory, computational methods, quantum computing and applications of machine learning to the physical sciences.

His teaching portfolio includes extensive open educational material, seminars and software. That combination of research and teaching shapes QTAI’s approach: explain the science clearly, examine the evidence and help people build their own understanding.

Explore Morten’s professional background

Academic affiliations describe professional background. QTAI’s consultancy services are independent.

Explore the foundations

Knowledge made accessible.

Explore the GitHub collection
Open educational material

Quantum computing
& machine learning

Lecture material and computational examples at the intersection of the two fields.

Lectures & examples

Advanced
machine learning

Methods and scientific applications for deeper technical exploration.

Course resources

Foundations of
machine learning

Teaching resources to support a practical understanding of core methods.

These academic resources are hosted independently and retain their original licences.

05 / Start a conversation

What are you
working towards?

Tell us about your organisation, the questions you’re exploring or the skills your team would like to develop.

mhjensen@uio.no

Contact Morten directly
Based in Norway · Enquiries welcome