Research
Led by a pan-Canadian network of universities and industry leaders, QAI4DO integrates the predictive analytics power of AI, the prescriptive analytics’ capability of OR, and the QC’s computational power, for building scalable solutions for complex, real-time challenges across transportation, logistics, healthcare, and more.
QAI4DO’s main research objective is to advance an integrated approach to Operations Research enabled by emerging AI & Quantum technologies. More specifically, QAI4DO aims to:
- Explore synergies between AI, QC and OR through new algorithms and techniques
- Design and develop methodologies and toolkits that enable such crossover research
- Expand theoretical and practical knowledge in three cutting-edge thematic research areas:
- ML for optimization (OR/AI)
- Quantum optimization (QC/OR)
- QML & AI decision making (QC/AI)
- Test and pilot the deliverables in case-studies and real world scenarios to solve data-and-computing intensive decision problems in diverse applications from healthcare to transportation, finance, supply chain management and more.
Applications of this research include, but are not limited to:
- Supply chain management
- Transportation and logistics
- Communication
- Robotics and autonomous systems
- Asset management
- Planning and scheduling
- Healthcare
- Finance and economics
- Sustainability
- Trustworthy, explainable and cyber-secured decision systems
Thematic Research Areas
Participants engage in interdisciplinary, theoretical or applied research in one of the three core research themes:
Machine Learning
for Optimization
Combining the descriptive, diagnostic, and predictive analytics of AI, particularly machine learning (ML), with the prescriptive analytics of OR is a promising approach to solving large-scale, real-time, (semi)automated (human-in-the-loop) decision optimization problems.
Quantum
Optimization
The intersection of OR and QC, which drives the development and use of adiabatic or gate-based quantum algorithms or hybrid classical quantum algorithms for solving optimization problems.
Quantum ML and
AI Decision Making
When QC intersects with ML algorithms, they create another revolutionary approach for boosting both the speed and performance of classical algorithms for solving complex optimization problems particularly on near-term quantum devices.
QAI4DO is not only advancing theoretical and applied research, it is building Canada’s future-ready workforce in quantum-AI enabled technologies. Trainees gain hands-on experience while contributing to real-world solutions that benefit various sectors and industries.
What Makes The QAI4DO Unique
- It is the only Canadian training initiative focused on the full integration of QC, AI, and OR for decision optimization.
- It offers interdisciplinary, cross-institutional mentorship supported by a national network of academic and industry partners.
- It provides hands-on experiential learning, including internships and access to advanced quantum infrastructure.
- It embeds equity, diversity, and inclusion (EDI) throughout the program, from trainee recruitment to research design, serving as a model for next-generation STEM training in Canada.
