2nd QuantOpt Workshop

GECCO 2023 @ Lisbon, 15th-19th July, 2023

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: 

 

All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) 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 will 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. Paper format should follow the GECCO 2023 instructions.



Important Dates