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.

Session overview

Sessions 1 and 2 explore how quantum-AI work gets done—through workflows, benchmarks, and the hybrid development stack. Session 3 asks who gets to participate and what must change to widen access. After three concise provocations, small groups draft actionable recommendations, debate them, and vote on 1–3 Quantum AI Resolutions. Adopted resolutions will be incorporated into the post-workshop white paper for funders, policymakers, and program designers.

  • Quantum AI: From Algorithms to Applications

    Part of the full-day workshop Democratizing Quantum AI: Practical Workflows, Benchmarks, and Hybrid Development for Quantum-Ready Applications

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    Date: Friday, September 18, 2026

    ‍Time: 10:00–11:30 am EDT

    Format: Expert talks → Q&A

    Focus

    Session 1 features two expert presentations followed by an audience Q&A. Dr. Kunal Sharma of IBM Research and Dr. Teague Tomesh of Infleqtion bring perspectives from quantum machine learning, quantum software engineering, and hardware-software co-design.

    ‍Featured Speakers

    • Dr. Kunal Sharma — Senior Research Scientist and Manager, IBM Research; leads work on quantum algorithms, quantum advantage, and quantum machine learning at IBM. Prior to joining IBM, he was a Hartree Postdoctoral Fellow at the University of Maryland. He also serves as an Editor for Quantum journal.

    • Dr. Teague Tomesh — Manager of Quantum Software Engineering, Infleqtion; leads a team developing quantum algorithms for applications in solid-state simulation, combinatorial optimization, and machine learning at Infleqtion. He earned his Ph.D. in Computer Science from Princeton University in 2023, following undergraduate studies in physics and astronomy, and brings an interdisciplinary perspective across physics, computer science, and quantum information.

    Agenda · All times EDT

    • 10:00–10:30: Learning Ground State Observables from Quantum Experiments
      Dr. Kunal Sharma, IBM Research ‍

    • 10:30–11:00: Leveraging Neutral Atom Quantum Technology for Life Sciences Applications
      Dr. Teague Tomesh, Infleqtion

    • 11:00–11:30: Q&A with the experts ‍

    Registration

    https://qce.quantum.ieee.org/2026/registration

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  • Quantum AI in Practice: Optimization and QML Workflows

    Part of the full-day workshop Democratizing Quantum AI: Practical Workflows, Benchmarks, and Hybrid Development for Quantum-Ready Applications

    Date: Friday, September 18, 2026
    Time: 1:00 - 2:30pm EDT
    Format: Expert talk → QML demo

    Focus

    Session 2 features an expert presentation from Dr. Yuri Alexeev of NVIDIA Corporation with a quantum machine learning demonstration led by Cascade Quantum.

    Featured Speakers

    • Dr. Yuri Alexeev — Senior Quantum Algorithm Engineer, NVIDIA Corporation; senior member of the IEEE Society. He has expertise in the development of quantum algorithms using the NVIDIA CUDA-Q framework and GPU-optimized quantum circuit simulators. Before joining NVIDIA, he worked at the Argonne Leadership Computing Facility to parallelize and optimize scientific codes for exascale supercomputers and the integration of high-performance computing with quantum computing. He completed PhD studies at Iowa State University and contributed to over 200 publications.

    • Cascade Quantum — A company building high-performance and user-friendly software platforms that unlock the full potential of quantum hardware.

    Agenda · All times EDT

    • 12:00–12:30: Evolutionary Agent for Solving Optimization Problems
      Dr. Yuri Alexeev, NVIDIA Corporation

    • 12:30–1:30: Quantum Machine Learning Demo
      Cascade Quantum — A hands-on demonstration of a quantum machine learning workflow, giving participants a practical look at how quantum-AI methods can be developed, executed, and explored using today's tools and computing environments.

    Registration

    https://qce.quantum.ieee.org/2026/registration

  • Date: Friday, September 18, 2026

    Time: 3:00–4:30 pm EDT

    Format: Panel perspectives → breakouts → live vote

    Moderator: Jamie VanDenbossche, CEO, Electron Networks

    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 — Quantum physicist and author; Professor, Wilfrid Laurier University; NSERC Chair for Women in Science & Engineering; CTO, Quantum Algorithms Institute; founding member, CRQIT.

      Dr. Kristen Csenkey — SSHRC Postdoctoral Fellow, Brian Mulroney Institute of Government; Postdoctoral Fellow, Canadian Maritime Security Network; Associate Fellow, Centre for Military, Security and Strategic Studies, University of Calgary.

      Dr. Sonika Johri — Co-Founder & CEO, Cascade Quantum; develops software platforms that connect users with expanding quantum hardware capabilities.

    Agenda·  All times EDT

    • 3:00–3:06: Welcome & framing
      Session purpose, drafting rules, adoption, and next steps.

    • 3:06–3:36: Panel provocations
      Ten minutes each: access and the scientific pipeline; governance, security, and trust; the software layer connecting users and hardware.

    • 3:36–4:06: Breakouts
      Groups of 4–6 tackle one curated challenge and draft one resolution using a shared template.

    • 4:06–4:21: Report-backs & synthesis.
      Ninety seconds per group; proposals are clustered and key disagreements surfaced.

    • 4:21–4:30: Debate & Vote.
      Participants vote on shortlisted proposals; adopted recommendations become the QCE26 Quantum AI Resolutions.

    ‍ Registration

    https://qce.quantum.ieee.org/2026/registration

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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.