Quantum Applications (QAPP) Paper Track Chairs
    Rudy Raymond, JPMorgan Chase — rudy.raymond@proton.me
    Martin Schulz, Technical University of Munich (TUM) — martin.w.j.schulz@tum.de

The practice of solving problems with quantum computers.

    Towards Quantum Advantage
    Towards fault tolerance and realization of quantum error correction at the application level
    Quantum Machine Learning (QML) applications
    Fault-tolerant applications
    Quantum simulation of physical systems
    Applications — chemistry, machine learning, finance, optimization, biological sciences, and other science & engineering applications
    Applications of quantum annealing
    Quantum for generative AI
    Integrated high-performance computing (HPC) and quantum applications
    Performance evaluation of quantum algorithms
    Optimization problems—transportation, supply chain & logistics
    Quantum AI & decision making
    Quantum medical applications & precision health applications
    Quantum DNA and protein sequencing
    Quantum finance

Quantum End-to-end Hybrid Case Studies (QECS) Paper Track Chairs
    Steven Rayan, University of Saskatchewan — steven.rayan@usask.ca
    Alexandre Choquette, IBM Quantum — alexandre@ibm.com

The design and implementation of domain-specific quantum computing case studies to demonstrate quantum utility.

    Domain-specific end-to-end quantum case studies
    Domain-specific problems and results
    Small-scale experiments
    Practical quantum simulation case studies in chemistry, materials, biology, or physics
    Domain-specific quantum optimization workflows, including combinatorial optimization, scheduling, or control problems, implemented using hybrid quantum–classical techniques.
    Data-driven insights from hybrid quantum experiments
    Validation through recovery of classical results, using known solutions
    Identification of nearby or approximate solutions, demonstrating how quantum methods can uncover useful suboptimal
    Error mitigation and robustness strategies applied within realistic end-to-end case studies, rather than in isolation
    Pathways to scale-up, including algorithmic generalization, problem size growth, hardware evolution, and integration with distributed or modular quantum systems
    Software engineering and tooling for hybrid case studies, including workflow orchestration, utility frameworks, reproducibility practices, and open-source implementations

Quantum–GenAI Co-Design & Co-Discovery (QGDD) Paper Track Chairs
    Yuri Alexeev, NVIDIA — yuri.alexeev@gmail.com
    Foutse Khomh, Polytechnique, Montréal — foutse.khomh@polymtl.ca

The practice of combining quantum computing and generative AI for innovative applications.

    Agentic quantum–generative AI systems and workflows
    Quantum-Generative AI co-design frameworks
    Quantum simulation data generation
    Drug discovery, materials science, biology, energy, or optimization enabled by QGDD pipelines
    Large quantitative models (LQMs) for domain-specific genAI
    Closed-loop discovery pipelines where genAI proposes candidates and quantum computers validate, refine, or falsify them
    Hybrid quantum-classical-AI system architectures
    Quantum-enhanced generative models, including variational quantum circuits, quantum Boltzmann machines, or hybrid quantum-AI generators
    Benchmarking, metrics, and evaluation methodologies for quantum–GenAI discovery workflows and co-designed systems
    Data representations and interfaces connecting quantum simulators, hardware backends, and generative AI models

Quantum Algorithms (QALG) Paper Track Chairs
    Ulrike Stege, University of Victoria — ustege@uvic.ca
    Stefan Woerner, IBM Quantum Zurich — wor@zurich.ibm.com

The theory of solving problems with quantum computers.

    Quantum information science
    Quantum algorithm structures and patterns
    Quantum algorithms and complexity
    Near-term quantum algorithms
    Error correction and mitigation algorithms
    Fault-tolerant quantum algorithms
    Advances in hybrid variational algorithms
    Advances in hybrid QAOA algorithms
    Quantum solver approaches
    Quantum linear algebra
    Tensor network algorithms
    Encoding and learning quantum algorithms
    Hamiltonian dynamics
    Quantum cryptography
    Secure quantum computing
    Privacy-preserving quantum computing

Quantum Technologies and Systems Engineering (QTEM) Paper Track Chairs
    Kasra Nowrouzi, QuNorth — kasra@qunorth.com
    Anthony Przybysz, Northrop Grumman — anthony.przybysz@ieee.org
    Tanay Roy, Fermi National Laboratory — roytanay@fnal.gov
 
The design and architecture of quantum technologies and systems engineering for computation and sensing.

    Superconducting quantum technologies
    Quantum annealing technologies
    Trapped ion quantum technologies
    Silicon quantum technologies
    Quantum dot technologies
    Neutral atom quantum technologies
    Topological quantum technologies
    Hardware-software stack for quantum annealers, trapped ions, superconducting, photonics, neutral atoms, and others
    Quantum characterization, verification & validation: benchmarking and tomography
    Qubit design and control
    Packaging and cooling
    Cryogenics
    Quantum electronics
    Pulse-level control of qubits
    Sensing and metrology
    Characterization and hardware mitigation of noise, state preparation and measurement errors

Quantum System Software (QSYS) Paper Track Chairs
    Shaukat Ali, Simula Labs — shaukat@simula.no
    Amir Shehata, Oak Ridge National Laboratory — shehataa@ornl.gov

The design, architecture, and operation of full-stack quantum computing systems.

    Full quantum software stack: compilers, runtimes, workflows, languages, transpilers, profilers
    Quantum programming, development kits (QDKs), test harnesses, debuggers
    Quantum languages and intermediate representations (IRs)
    Quantum simulators
    Quantum software engineering
    Generative AI in quantum software development and systems software
    Software for co-design
    Hybrid quantum-classical systems
    Resource estimation
    Quantum control software
    Interfacing classical control and quantum hardware through software
    Error correction and mitigation
    Fault-tolerant computing at the system software level
    Testing, validation, and verification of quantum programs and systems
    Benchmarking of quantum systems, quantum volume and other metrics
    Software techniques for error correction and noise mitigation
    Hardware-software stacks for error mitigation
    Quantum software in enterprise systems
    Secure quantum systems
    Privacy-preserving quantum systems

Quantum Photonics (QPHO) Paper Track Chairs
    Luu Nguyen, PsiQuantum — lnguyen@psiquantum.com
    Jaqui Romero, University of Queensland — m.romero@uq.edu.au

The design and architecture of quantum photonic technologies and systems engineering.

    Quantum photonic information science and technology
    Quantum computing with photonic systems
    Quantum entanglement and teleportation
    Optical quantum computing
    Photonic quantum technologies
    Photonic quantum computers
    Integrated quantum photonics
    Photonics-based qubit technologies
    Photonic quantum simulation
    Silicon photonic devices
    Photon sources and detectors
    Quantum sensing and metrology

Quantum Machine Learning (QML) Paper Track Chairs
    Sarah Chehade, University of Tennessee at Chattanooga — sarah-chehade@utc.edu
    Arno Jacobsen, University of Toronto — arno.jacobsen@gmail.com

The practice of combining quantum computing and machine learning for innovative application development.

    Quantum algorithms for machine learning tasks
    AI-assisted quantum information science
    Quantum-enhanced machine learning
    Quantum-inspired models and machine learning
    Quantum Boltzmann Machines
    Quantum Neural Networks (QNNs)
    Quantum Support Vector Machines (QSVMs)
    Quantum Generative Adversarial Networks (QGANs)
    Quantum Generative AI
    Training machine learning models
    Quantum algorithms for reinforcement learning
    Quantum clustering and classification
    Advances in encoding and learning algorithms
    Benchmarking of quantum systems, quantum volume and other metrics
    Quantum machine learning theory
    Quantum error correction and mitigation
    Fault-tolerant QML applications
    Quantum state tomography
    Quantum machine learning applications
    QML software and libraries
    QML benchmarks

Quantum Networking & Communications (QNET) Paper Track Chairs 
    Angela Sara Cacciapuoti, University of Naples Federico II — angelasara.cacciapuoti@unina.it
    Rajkumar Kettimuthu, Argonne National Laboratory — kettimut@mcs.anl.gov

 Quantum techniques and technologies for networking and communications

    Quantum internet
    Quantum networking
    Quantum switches, routers, repeaters, and other hardware components
    Signal processing algorithms for quantum communication
    Optical quantum communications
    Intra-chip and inter-chip communication
    Secure communication in quantum networks
    Quantum cryptography
    Quantum key distribution (QKD)
    Post-quantum cryptography
    Distributed quantum computing
    Cloud quantum computing
    Secure quantum computing
    Privacy-preserving quantum protocols