3rd Workshop on
Quantum Computer Vision and Machine Learning

Key Information:

• Date: Sep. 9th, PM
• Location: TBD

What is Quantum Computing?

Quantum computing (QC) is an emerging computing paradigm that harnesses quantum-mechanical phenomena, such as superposition and entanglement, to process information. Unlike classical computers that operate on binary bits, quantum computers use qubits, enabling fundamentally different approaches to optimization, simulation, and sampling. While today's quantum devices remain in the Noisy Intermediate-Scale Quantum (NISQ) era, rapid advances in both hardware and algorithms are making practical QC increasingly accessible.

What is Quantum Computer Vision & Machine Learning?

Quantum Computer Vision and Machine Learning (QCVML) explores how QC can complement modern AI systems. Rather than replacing deep learning, quantum algorithms are designed to work alongside classical models, enabling hybrid quantum–classical workflows. Current research investigates applications including optimization, geometric estimation, clustering, graph matching, image generation, 3D scene representation, and multimodal learning.

Why Quantum Generative Models?

Generative AI has become a cornerstone of modern computer vision, powering diffusion models, image synthesis, video generation, neural scene representations, and large multimodal models. At the same time, these models require enormous computational resources for training and inference. Quantum generative models—including quantum GANs, quantum diffusion models, Born machines, and hybrid quantum–classical architectures—are emerging as a promising research direction for learning and sampling high-dimensional distributions, with the potential to improve computational efficiency and scalability as quantum hardware matures.

Why Now?

Computer vision has repeatedly been transformed by new computing hardware—from CPUs to GPUs and AI accelerators. QC represents the next frontier. Significant progress in quantum hardware, software frameworks, and algorithms has enabled the first demonstrations of practical computer vision applications on both quantum annealers and gate-based quantum processors. Meanwhile, the computational and energy demands of large-scale generative AI continue to grow rapidly, making this an ideal time to explore where QC can provide genuine advantages and establish realistic benchmarks for future research.

Workshop Vision?

The 3rd Workshop on Quantum Computer Vision and Machine Learning (QCVML) provides an accessible entry point for the ECCV community into this rapidly growing field. The workshop spans both quantum annealing and gate-based QC, with a special focus on quantum generative models for vision and multimodal learning. Through invited talks, tutorials, and discussions, QCVML aims to connect researchers from computer vision, machine learning, QC, physics, and industry, fostering collaboration and shaping the future of quantum-enhanced vision systems.

Why Attend?

• Learn QC from a computer vision perspective—no prior quantum background required.
• Discover the latest progress in quantum generative AI, diffusion models, and hybrid quantum–classical learning.
• Understand the capabilities and limitations of current quantum hardware.
• Hear from leading researchers from academia, industry, and national laboratories.
• Meet researchers working at the intersection of computer vision, machine learning, and QC.

• Provide a structured introduction to quantum generative modelling from a computer vision perspective.
• Unify emerging approaches, including quantum GANs, quantum diffusion models, quantum implicit representations, and hybrid quantum–classical generators.
• Identify vision and multimodal applications where quantum methods may complement classical models.
• Clarify what is feasible today on quantum hardware and simulators, and discuss limitations in noise, trainability, scalability, and resource requirements.
• Promote fair evaluation, reproducible benchmarks, and transparent comparisons with quantum and classical baselines.
• Connect researchers across computer vision, machine learning, physics, quantum algorithms, and quantum hardware.

• Foundations of quantum computing for computer vision researchers
• Quantum annealing and gate-based quantum computing for CVML
• Quantum generative adversarial networks
• Quantum diffusion and latent diffusion models
• Hybrid quantum–classical generative architectures
• Quantum implicit neural representations and 3D scene modelling
• Quantum hardware, simulators, software frameworks, and energy trade-offs
• Noise mitigation, trainability, scalability, and hardware-aware design
• Evaluation metrics, benchmarks, reproducibility, and reviewing standards
• Practical pathways towards quantum advantage in computer vision

QCVML 2026 does not host a paper or poster submission track. Instead, we continue the successful Highlight Talks session from previous editions, providing a platform to showcase and discuss recent advances in quantum generative models for computer vision and machine learning.

Authors of recently published, peer-reviewed papers are encouraged to contact the organizing committee to propose a highlight talk. Topics of interest include, but are not limited to:

• Quantum diffusion models
• Quantum generative adversarial networks (QGANs)
• Quantum variational autoencoders and latent-variable models
• Hybrid quantum-classical generative architectures
• Quantum-enhanced representation learning for generation
• Theoretical foundations and scalability of quantum generative learning

Preference will be given to recent publications at leading venues in computer vision, machine learning, quantum computing, and quantum information. Interested authors are encouraged to contact the organizing committee at frances.yfy@gmail.com with a brief summary of their work and publication details.