Topics
Proposals may cover one of the following topics (but are not limited to these):
Industry Application & Adoption (AI & Quantum)
- Use cases, pilots and lessons learned across key sectors with a particular focus on automotive & mobility, finance, health & life sciences, and manufacturing
- Progress from PoCs towards real-world deployment and industrial relevance
- Cross-cutting AI × Quantum opportunities and constraints in practice
- Organisational and operational readiness: skills, roles, sourcing, partnerships
Benchmarking, Metrics & Evaluation (AI & Quantum)
- AI: model evaluation and benchmarking (quality, robustness, safety), compute/data efficiency, lifecycle KPIs
- Quantum: benchmarking and validation of systems/workloads, comparability metrics, resource estimation & feasibility
- Evaluation methodologies, standards, and decision-relevant evidence
Quantum Stack & Systems Progress
- End-to-end progress across hardware, control, software toolchains and algorithms
- System engineering bottlenecks, reliability constraints and integration challenges
- Scaling pathways: error mitigation, error correction and fault tolerance
- Architectural trade-offs, milestones and performance indicators
Hybrid Quantum Integration (Quantum–HPC–Classical)
- Hybrid workflows, orchestration and runtime integration
- Integration of Quantum Computing into existing HPC, cloud and IT environments
- Toolchains, interfaces, compilers/runtimes and workflow design
- Data movement, latency, operational constraints and deployment models
Quantum Security & Quantum-Safe Infrastructure
- Post-quantum cryptography migration: timelines, approaches and readiness
- Quantum security implications for organisations and critical infrastructure
- Quantum communication where relevant to security and infrastructure
AI Systems, Data & MLOps
- Foundation models, applied AI, agentic and multimodal systems
- Data products and spaces, governance, quality, data sharing and interoperability
- AI-assisted and AI-native development workflows (vibe coding, agentic coding, etc.)
- AI infrastructure and compute architectures (energy, efficiency, scaling, etc.)
- MLOps and lifecycle management: monitoring, drift, evaluation, reliability
Trustworthy AI, Compliance & Secure AI Engineering
- Governance and compliance operationalisation (incl. AI Act)
- Risk management, documentation, auditing and accountability
- Secure AI engineering: robustness, adversarial threats, security-by-design
- Responsible deployment in regulated and high-stakes contexts
