[GECCO CFP] 2nd Workshop on Quantum Optimization (QuantOpt) @ GECCO 2023

[Alberto Moraglio]

*CALL FOR PAPERS*

*QuantOpt**@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*

*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'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
https://ssl.linklings.net/conferences/gecco/. Please refer to
https://gecco-2022.sigevo.org/Paper-Submission-Instructions for more
detailed instructions.



*Workshop Chairs *

·        Alberto Moraglio, University of Exeter, UK, a.moraglio at exeter.ac.uk

·        Mayowa Ayodele, Fujitsu Laboratories of Europe, UK,
mayowa.ayodele at fujitsu.com

·        Francisco Chicano, University of Malaga, Spain, chicano at lcc.uma.es

·        Oleksandr Kyriienko, University of Exeter, UK,
o.kyriienko at exeter.ac.uk

·        Ofer Shir, Tel-Hai College and Migal Institute, Israel,
ofersh at telhai.ac.il

·        Lee Spector, Amherst College, USA, lspector at amherst.edu
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