IEEE Quantum AI Workshop

About the Workshop

The potential of quantum artificial intelligence (AI) has attracted growing interest across several scientific computing and applied research domains, yet exploration of practical use cases remains limited by high infrastructure costs, technical complexity, restricted access to hardware and cloud resources, and immature development workflows for practitioners trained primarily in classical Artificial Intelligence (AI) and software engineering.

This workshop will focus on the transition now underway from basic research to practical, industry-relevant applications. It will introduce software tools that enable hybrid quantum-classical workflows on cloud-accessible quantum computers. Effective use of these tools can significantly lower barriers for developers, researchers, startups, and small and medium-sized enterprises seeking to build quantum-enhanced AI.

The workshop will address practical quantum AI broadly, including both discriminative and generative learning. It will teach fundamentals about processing a variety of data modalities—such as images, point clouds, time series, text, and biological data—into a form ready for loading into a quantum computer. Participants will explore the best optimization strategies for quantum models and learn how to solve issues such as barren plateaus and local minima, which often lead to sub-optimal results in quantum machine learning (QML) pilot projects.

A central theme is democratization: expanding meaningful access to quantum development beyond a small set of highly capitalized or highly specialized institutions. Through invited talks, demonstrations, moderated discussion, and roadmap-oriented interaction, the workshop will identify practical barriers, emerging solutions, and priorities for making quantum AI more accessible, reproducible, and relevant to real-world applications.

Key Objectives

  • Bridge the Research-to-Application Gap: Examine how quantum AI transitions from theoretical research toward practical, real-world industrial use cases.

  • Democratize Development: Introduce accessible hybrid quantum-classical workflows and software-enabled pathways to accelerate technology adoption.

  • Identify Ideal Use Cases: Help participants identify which specific problem types, datasets, and industrial contexts are most suitable for near-term, quantum-enhanced approaches.

  • Establish Rigorous Benchmarking: Provide actionable guidance on model validation and performance comparison against strong classical baselines.

  • Close the Access Gap: Address the economic and technical barriers that currently limit meaningful quantum experimentation to a small set of highly capitalized institutions.

  • Foster Cross-Disciplinary Collaboration: Build a common language between quantum researchers, software engineers, classical AI practitioners, startups, SMEs, and enterprise teams.

  • Shape the Industry Roadmap: Contribute insights toward a broader, scalable framework for accessible, quantum-ready software applications.

  • Focus: Examining where the field stands today, emerging real-world opportunities, and identifying structural barriers (cost, complexity, access) to adoption.

    • Invited Talks (3 x 15 mins): Featuring researchers applying quantum AI to healthcare, finance, and manufacturing.

    • Panel Discussion (30 mins): 4–5 AI experts uncovering unique sectoral challenges and potential integrations with quantum communication.

  • Focus: Executable practice, hybrid quantum-classical workflows, model design, and a hands-on technical deep dive.

    • Industry Insights: Talks from a quantum software platform developer and a quantum hardware provider.

    • Hands-On Tutorial (50 mins): A step-by-step walkthrough on identifying datasets, mapping data to quantum states, designing a quantum neural network, and training it to high accuracy. (Data sets available for download pre-workshop).

  • Focus: Political, structural, and economic frameworks needed to scale quantum-ready AI responsibly across startups, SMEs, and R&D teams.

    • Featured Speakers:

      • Dr. Shohini Ghose (Wilfrid Laurier University / Quantum Algorithms Institute) discussing responsible innovation and ecosystem access.

      • Dr. Kristen Csenkey (St. Francis Xavier University) addressing public policy, security, and governance considerations.

    • Roadmap Roundtable: A moderated discussion mapping out future investments, standards, and cloud-access pathways.

Who Should Attend

This workshop is designed for QML researchers, application developers, software engineers, and classical data scientists. Educators, startups, SMEs, and enterprise R&D teams evaluating practical pathways to quantum-ready AI will benefit most. No advanced background in quantum physics is required—basic programming and data science familiarity is sufficient.

Expected Outcomes

Beyond building an interdisciplinary community, this workshop will directly produce a collaborative White Paper. This publication will synthesize identified technical barriers, open research questions, and priority actions for making quantum-ready applications accessible and reproducible worldwide.