Monday, February 20, 2023

[DMANET] CFPs for 2nd Workshop on Quantum Optimization @ GECCO 2023



2nd Workshop on Quantum Optimization

Genetic and Evolutionary Computation Conference (GECCO'23)

Lisbon, Portugal, July 15-19, 2023

Paper Submission Deadline: April 14, 2023


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.


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'23) Companion Volume.

Key Dates
Submission Opening: February 13, 2023
Paper Submission Deadline: April 14, 2023
Notification of Acceptance: May 3, 2023
Camera-Ready Copy Due: May 10, 2023
Author Registration: May 10, 2023
Conference Presentation: 15 July 2023 to 19 July 2023

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 <>. Please refer to <> for more detailed instructions.

Workshop Chairs

· Alberto Moraglio, University of Exeter, UK, <>
· Mayowa Ayodele, Fujitsu Laboratories of Europe, UK, <>
· Francisco Chicano, University of Malaga, Spain, <>
· Oleksandr Kyriienko, University of Exeter, UK, <>
· Ofer Shir, Tel-Hai College and Migal Institute, Israel, <>
· Lee Spector, Amherst College, USA, <>

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