[Opt-Net] 2nd CFP for 4th Workshop on Quantum Optimization @ GECCO 2025 (Malaga, Spain)

[chicano]

(apologies for crossposting)

                             2nd CALL FOR PAPERS
                             QuantOpt at GECCO-2025
                    4th Workshop on Quantum Optimization
         Genetic and Evolutionary Computation Conference (GECCO'25)
                       Malaga, Spain, July 14-18, 2025

                  Paper Submission Deadline: March 26, 2025

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 optimization is an 
emerging field that focuses on using quantum computing technologies to 
solve hard optimization problems.

There are two main types of quantum computers: quantum annealers and 
gate-based quantum computers. Quantum annealers are specially tailored 
to solve combinatorial optimization 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 optimization problems using the quantum approximate 
optimization 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 optimization 
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 optimization 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 optimization problem as 
a Quadratic Unconstrained Binary Optimization (QUBO) problem. This is 
closely related to the renowned Ising model. It constitutes a universal 
class, since all combinatorial optimization problems can be formulated 
as QUBOs. In practice, some classes of optimization problems can be 
naturally mapped to a QUBO, whereas others are much more challenging to 
map.


Content

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

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

Particular topics of interest include, but are not limited to:
·        Formulation of optimization 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 optimization problems for quantum hardware
·        Application of the quantum approximate optimization algorithm
·        Application of Grover's algorithm to solve optimization 
problems
·        Novel quantum-inspired optimization algorithms
·        Optimization/discovery of quantum circuits
·        Quantum optimization for machine learning problems
·        Optical Annealing
·        Dealing with noise in quantum computing
·        Quantum Gates’ optimization, Quantum Coherent Control

All accepted papers of this workshop will be included in the Proceedings 
of the Genetic and Evolutionary Computation Conference (GECCO'25) 
Companion Volume.

Instructions for Authors

We invite submissions of two types of paper:
·        Regular papers (limit 8 pages)
·        Short papers (limit 4 pages)

Papers should present original work that meets the high-quality 
standards of GECCO. Each paper will be rigorously evaluated in a review 
process. Accepted papers appear in the ACM digital library as part of 
the Companion Proceedings of GECCO. Each paper accepted needs to have at 
least one author registered by the author registration deadline. Papers 
must be submitted via the online submission system 
https://ssl.linklings.net/conferences/gecco/. Please refer to 
https://gecco-2025.sigevo.org/Paper-Submission-Instructions for more 
detailed instructions.


Workshop Chairs

- Alberto Moraglio, University of Exeter, UK
- Mayowa Ayodele, D-Wave systems, UK
- Francisco Chicano, University of Malaga, Spain
- Ofer Shir, Tel-Hai College and Migal Institute, Israel
- Lee Spector, Amherst College, USA
- Matthieu Parizy Fujitsu Limited, Japan