CVPR Workshop QCVML 2023

What is a quantum computer?

A quantum computer (QC) is a computing machine which takes advantage of quantum effects such as quantum superposition, entanglement, tunnelling and contextuality to solve problems notoriously difficult (belonging to challenging complexity classes such as NP) for a classical computer. Thanks to the exponentially increasing investment in the technology, quantum computers are gradually moving from the realms of theory towards actual devices. Albeit restricted, experimental realisations of numerous quantum algorithms have demonstrated improved computational performance finally reaching the desired supremacy in recent years. To date, IBM has constructed a device with 65 qubits and D-Wave has constructed a computer based on quantum annealing having >5000 qubits. Quantum computers offer speed-ups on certain specific algorithms including well known examples such as Shor’s algorithm, the quantum fourier transform (QFT), and, in the case of quantum annealing, solving non-convex, quadratic unconstrained binary optimization (QUBO) problems. This makes them amenable for deployment in the context of machine learning and computer vision, which demand significant amounts of compute resources. For example, visual perception algorithms need to process millions of pixels, or modern deep learning frameworks have millions of parameters to optimise for. The application of quantum computation to computer vision and machine learning is very interesting and intriguing because of both speed-up benefits and global optimality guarantees it offers.

What do we mean by quantum computer vision?

The aforementioned premises of quantum computing and annealing has led to the popularisation of quantum computer vision (QCV), where researchers started to port existing computer vision problems into forms amenable to quantum computation. However, reframing the existing problems in the context of this new computing paradigm is not trivial. For instance, existing literature relaxes most of the (discrete) combinatorial search problems to continuous ones, whereas quantum annealing is great at optimisation on discrete binary variables. Hence, oftentimes we are required to revise the problem formulations at hand altogether. The silver lining of this seemingly bad news is the novel research directions it opens up. In the past few years, we have started to witness the development of the first archetypes of this new area. Modern quantum computation has penetrated the realm of computer vision and machine learning in various subjects of study from point cloud registration to multi-object tracking with well supported industry grade development platforms such as D-Wave Ocean, Tensorflow Quantum, IBM Qiskit and Amazon Braket.

Why organize QCVML now?

We are optimistic that the quantum revolution is around the corner and the time has come. However, are we, as a community, prepared for this disruption? Our proposed workshop, QCVML (Quantum Computer Vision and Machine Learning) will be dedicated to investigating computer vision and machine learning problems, theoretically and experimentally, through the lens of practical quantum computation. Our main objective is to gather together industry experts, academic researchers, and CV-practitioners of quantum computing into a lively environment for discussing methodologies and challenges raised in exploiting these new types of computing devices; as a targeted topic venue, this workshop will offer participants a unique opportunity to network with a diverse but focused research community.

5 Speakers
1 Day

Goals & Themes

The goal of this workshop is to introduce quantum computation to the realm of computer vision and foster the formation of a community. A concrete summary of the aims are as follows:
  • Identify computer vision problems that can be addressed by quantum computers
  • Showcase recent and ongoing progress towards practical quantum computing and computer vision
  • Address and discuss the current state-of-the art, limitations therein, expected progress and its impact on the computer vision world
  • Enlighten the community to attract further researchers in this direction
Focal points for discussions and talks include but are not limited to:
  • Premises of quantum computation
  • Use of the techniques from quantum mechanics in solving CVML problems, classically
  • Adiabatic quantum computation and use cases in CVML
  • Circuit based quantum computers and their use in CVML
  • Tensor methods in QCVML
  • Review of the upcoming software for programming QC

Our speakers

Our invited speakers come from top research institutions and companies around the globe, and are leading figures in the topics covered by the workshop. This diverse selection will prove valuable for academic as well as industry researchers and practitioners. Both practical and theoretical aspects of quantum computing and its use in computer vision will be covered by the invited lecturers. We will make a selection out of the confirmed speakers, with a potential to include unconfirmed speakers. Both of these are listed below.

<Tat-Jun Chin


University of Adalaide

<Victoria Horan Goliber

Horan Goliber

Global Head of Tech. Sales

<Roberto Bondesan


Senior Lecturer
Imperial College London

<Anand Rangarajan


University of Florida

Michael Moeller


University of Siegen

The schedule

We have a packed and exciting day ahead of us!


We collected recent papers that target the computer vision community and are relevant to the field.

The following works have previously been published and peer-revieweed at major computer vision and machine learning conferences.

Adiabatic Quantum Graph Matching with Permutation Matrix Constraints

Authors: Marcel Seelbach Benkner, Vladislav Golyanik, Christian Theobalt, Michael Moeller

Publication Venue: 3DV, 2020

Quantum Permutation Synchronization

Authors: Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas J. Guibas

Publication Venue: CVPR, 2021

Q-Match: Iterative Shape Matching via Quantum Annealing

Authors: Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Christof Wunderlich, Christian Theobalt, Michael Moeller

Publication Venue: ICCV, 2021

Adiabatic Quantum Computing for Multi Object Tracking

Authors: Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc Van Gool

Publication Venue: CVPR, 2022

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Authors: Anh-Dzung Doan, Michele Sasdelli, David Suter, Tat-Jun Chin

Publication Venue: CVPR, 2022

Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization

Authors: Alp Yurtsever, Tolga Birdal, Vladislav Golyanik

Publication Venue: ECCV, 2022

Quantum Motion Segmentation

Authors: Federica Arrigoni, Willi Menapace, Marcel Seelbach Benkner, Elisa Ricci, Vladislav Golyanik

Publication Venue: ECCV, 2022

Quantum-soft QUBO Suppression for Accurate Object Detection

Authors: Junde Li, Swaroop Ghosh

Publication Venue: ECCV, 2020

QuAnt: Quantum Annealing with Learnt Couplings

Authors: Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik

Publication Venue: ICLR, 2023

CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes

Authors: Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt, Vladislav Golyanik

Publication Venue: CVPR, 2023

Quantum Multi-Model Fitting

Authors: Matteo Farina, Luca Magri, Willi Menapace, Elisa Ricci, Vladislav Golyanik, Federica Arrigoni

Publication Venue: CVPR, 2023

A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer

Authors: Hongyi Pan, Xin Zhu, Salih Atici, Ahmet Enis Cetin

Publication Venue: ICML, 2023

The following manuscripts are technical reports of work in progress.

A Universal Quantum Algorithm for Weighted Maximum Cut and Ising Problems

Authors: Natacha Kuete Meli, Florian Mannel, Jan Lellmann

Work in Progress

QRF: Implicit Neural Representations with Quantum Radiance Fields

Authors: Yuan-Fu Yang, Min Sun

Work in Progress


Here are the diligent people behind QCVML 2023.

Tolga Birdal


Assistant Professor
Imperial College London

Vladislav Golyanik


Research Group Leader

Jacob Biamonte



Martin Danelljan


Group Leader
ETH Zurich

Tongyang Li


Assistant Professor
Peking University

Tongyang Li


PhD Candidate
ETH Zurich


  • Why arrange a Quantum Computer Vision workshop now and why should I attend?

    A small community in Quantum Computer Vision has formed over the last years and is steadily growing. With quantum computers being easily accessible by now, we believe that the time is right to show the potential of quantum computer vision and to bring together researchers from both fields.

    Attending the workshop is a great opportunity to gain a glimpse on the basics in quantum computing. Furthermore, invited talks from academia and industry will give an overview of the current developments of the field and our poster session will give you the opportunity to see the current state-of-the-art collected in one place.

  • Is the workshop fully in-person?

    Yes, QCVML will be held fully in-person on 18. July at CVPR 2023. While there will be no live streaming, we will record the talks and make them available on the website. Furthermore, an extended version of the poster session will be presented on the website.

  • Does QCVML accept paper submissions?

    No. Quantum computer vision is a very new area and we are hoping to solicit papers in the next versions of this workshop.

  • How have the posters been selected?

    The poster session comprises papers previously peer-reviewed and published at major Computer Vision or Machine Learning conferences, focusing on topics closely related to Quantum Computer Vision. In addition to this, technical reports that show work-in-progress by researches that previously published in the field of quantum computer vision are also presented. These works will be clearly indicated as such.

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QCVML 2023 Venue

  • 1055 Canada Pl, Vancouver, BC V6C 0C3, Canada
  • E-mail :