2nd Workshop on AI for Quantum:
Algorithms & Applications
Emerging AI-driven paradigms for quantum algorithm development — agentic algorithm discovery, generative models for generalization, and AI-enabled state preparation and parameter transfer.
Workshop overview
The development of practical quantum algorithms is increasingly limited not only by hardware constraints, but by the difficulty of discovering, optimizing, and generalizing algorithms across problem instances, system sizes, and hardware backends.
Artificial intelligence is rapidly emerging as a transformative tool for addressing these challenges — enabling automated exploration of quantum algorithm spaces that are far beyond human intuition. This workshop focuses on agentic AI for algorithm discovery, generative models for algorithm generalization, and AI-driven techniques for state preparation and parameter transfer.
We explore how large language models, evolutionary strategies, reinforcement learning, and hybrid agentic workflows can discover new quantum circuits, optimize variational algorithms such as VQE and QAOA, and transfer learned structures and parameters across problem families and hardware platforms. The workshop highlights end-to-end workflows combining AI, HPC, and quantum execution — covering both near-term (NISQ) and longer-term fault-tolerant settings.
By bringing together researchers from quantum algorithms, AI / ML, HPC, and quantum engineering, the workshop aims to identify common challenges, shared abstractions, and concrete opportunities for collaboration. The session emphasizes actionable lessons, open research questions, and community-driven directions for advancing AI-native quantum algorithm design.
Keywords
Agentic AI · Foundation Models · Scalable Quantum Computing · Generative AI · Large Language Models · Diffusion Models · Reinforcement Learning · Parameter Transfer · VQE · QAOA
Goals
Short-term
- Expose attendees to state-of-the-art AI techniques for quantum algorithm discovery
- Compare different AI paradigms for quantum workflows
- Identify practical bottlenecks in data generation, training, and quantum execution
Long-term
- Establish a sustained research community around AI-native quantum algorithm engineering
- Develop shared benchmarks, datasets, and evaluation metrics
- Enable cross-pollination between AI, HPC, and quantum algorithm communities
Schedule
Friday, September 18, 2026 · Toronto, Canada
Speakers
Organizers
Works on hybrid quantum-classical algorithm and AI-assisted quantum algorithm design for scalable, real-world applications using the CUDA-Q platform.
mfarag@nvidia.com
Specializes at the intersection of quantum computing, HPC, and AI. Works with NVIDIA CUDA-Q to build scalable hybrid quantum-classical applications and algorithms.
porao@nvidia.com
Works on quantum computing algorithms, error correction/mitigation, and numerical simulations using CUDA-Q and accelerated quantum supercomputing.
yalexeev@nvidia.com
NVIDIA Quantum Developer Relations Manager for Supercomputing centers. Holds a PhD in Theoretical Chemistry from the University of Liverpool and is an Honorary Senior Research Fellow there.
smensa@nvidia.com
Develops AI-driven approaches for Quantum Chemistry: state preparation, molecular Hamiltonian representations, and electronic structure theory.
jem.guhit@quantinuum.com
Works at the intersection of AI, quantum computing, and quantum chemistry. PhD from MIT Center for Theoretical Physics; previously senior researcher at CERN and Oxford.
jasmine.brewer@quantinuum.comGet in touch
For questions about the workshop, speakers, or logistics, contact the main organizer:
Dr. Marwa Farag — Main point of contact
Senior Quantum Algorithm Engineer, NVIDIA
mfarag@nvidia.com
Workshop page on IEEE Quantum Week 2026: qce.quantum.ieee.org/2026/workshops/
Previous edition: AI4Q 2025 (QCE25, Albuquerque)