Overview and Scope

Quantum computers are rapidly becoming more powerful and increasingly applicable to solve problems in the real world. They have the potential to solve extremely hard computational problems, which are currently intractable by conventional computers. Quantum optimisation is an emerging field that focuses on using quantum computing technologies to solve hard optimisation problems.

There are two main types of quantum computers: quantum annealers and gate-based quantum computers. Quantum annealers are specially tailored to solve combinatorial optimisation problems. They find (near) optimal solutions via quantum annealing, which is similar to traditional simulated annealing, and use quantum tunnelling phenomena to provide a faster mechanism for moving between states and faster processing. On the other hand, gate-based quantum computers are universal and can perform general purpose calculations. These computers can be used to solve combinatorial optimisation problems using the quantum approximate optimisation algorithm and quantum search algorithms. 

Quantum computing has also given rise to quantum-inspired computers and algorithms. Quantum-inspired computers use dedicated hardware technology to emulate/simulate quantum computers. Quantum-inspired optimisation algorithms use classical computers to simulate some physical phenomena such as superposition and entanglement to perform quantum computations, in an attempt to retain some of its benefit in conventional hardware when searching for solutions.

To solve optimisation problems on a quantum computer, we need to reformulate them in a format suitable for the quantum hardware, in terms of qubits, biases and couplings between qubits. In mathematical terms, this requirement translates to reformulating the optimisation problem as a Quadratic Unconstrained Binary Optimisation (QUBO) problem. This is closely related to the renowned Ising model. It constitutes a universal class, since all combinatorial optimisation problems can be formulated as QUBOs. In practice, some classes of optimisation problems can be naturally mapped to a QUBO, whereas others are much more challenging to map.

The aim of the workshop is to provide a forum for both scientific presentations and discussion of issues related to quantum optimisation. As the algorithms that quantum computers use for optimisation can be regarded as general types of heuristic optimisation algorithms, there are potentially great benefits and synergy to bringing together the communities of quantum computing and heuristic optimisation for mutual learning.  

Topics of Interest

The workshop aims to be as inclusive as possible, and welcomes contributions from all areas broadly related to quantum optimisation, and by researchers from both academia and industry.

Particular topics of interest include, but are not limited to: 

• Formulation of optimisation problems as QUBOs (including handling of non-binary representations and constraints)

• Fitness landscape analysis of QUBOs

• Novel search algorithms to solve QUBOs

• Experimental comparisons on QUBO benchmarks 

• Theoretical analysis of search algorithms for QUBOs 

• Speed-up experiments on traditional hardware vs quantum(-inspired) hardware

• Decomposition of optimisation problems for quantum hardware

• Application of the quantum approximate optimisation algorithm

• Application of Grover's algorithm to solve optimisation problems

• Novel quantum-inspired optimisation algorithms

• Optimisation/discovery of quantum circuits

• Quantum optimisation for machine learning problems

• Optical Annealing

• Dealing with noise in quantum computing

• Quantum Gates’ optimisation, Quantum Coherent Control

All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'26) Companion Volume.

Important Dates

• Submission opening: February 2, 2026

• Submission deadline: March 27, 2026

• Notification: April 24, 2026

• Camera-ready: May 5, 2026

• Author’s mandatory registration: May 11, 2026

• Workshop date: July 13 or 14, 2026 (TBC)