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.

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

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.

The following manuscripts are technical reports of work in progress.