Project:	Challenging Quantum Computing on QUBOS - SURF@Newcastle in 2025
Disciplines:	Computation and Neural Systems, Mathematics, CS, Applied Math, Physics
Mentor:	Pablo Moscato, Professor, (EAS), pablo.moscato@newcastle.edu.au, Phone: +61 2 424216209
Mentor URL:	https://www.newcastle.edu.au/profile/pablo-moscato  (opens in new window)
Background:	NOTE: This project is being offered by a Caltech alum and is open only to Caltech students. The project will be conducted at the University of Newcastle in Newcastle, Australia. As quantum computing transitions from theoretical exploration to practical applications, there is a growing need for standardized benchmarks and competitions to assess the performance of quantum algorithms and hardware. This project envisions the creation of a structured, competitive environment for quantum computing—where researchers can test and compare quantum algorithms against their classical counterparts on a given problem. By fostering a culture of competition and collaboration, this initiative aims to accelerate the development of quantum technologies and push the boundaries of what can be achieved with quantum computers in fields such as cryptography, optimization, and machine learning. The Quadratic Unconstrained Binary Optimization (QUBO) problem is a fundamental challenge in combinatorial optimization that involves minimizing a quadratic function over binary variables. QUBO problems are NP-hard and arise in many practical contexts, including finance, telecommunications, and energy management. Recently, QUBO has gained prominence in the quantum computing community as a class of problems that can be effectively tackled by quantum algorithms. This project will explore the mathematical foundations of QUBO, as well as its applications in quantum computing and classical optimization methods, providing students with the opportunity to work on cutting-edge problems in optimization theory. It is expected to develop a state-of-the-art memetic algorithm solver for QUBO instances. [This project may have more than one individual, so working collaborators are invited to apply as a team.]
Description:	This is a project that suits well a highly skilled programmer, who has a keen interest in the development of the world’s fastest classical algorithm solver for QUBO instances based on memetic algorithms.
References:	1) What is a QUBO? https://support.dwavesys.com/hc/en-us/articles/360003684474-What-Is-a-QUBO 2) Qbsolve - https://github.com/dwavesystems/qbsolv 3) Memetic algorithms for the Unconstrained Binary Quadratic Problem, by Peter Merz and Kengo Katayama, Biosystems, Vol. 78, Issues 1-3, Dec. 2004, pp. 99-118. https://www.sciencedirect.com/science/article/abs/pii/S0303264704001376 4) A Multilevel Algorithm for Large Unconstrained Binary Quadratic Optimization, by Yang Wang, Zhipeng Lü, Fred Glover & Jin-Kao Hao, Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems, Springer, https://link.springer.com/chapter/10.1007/978-3-642-29828-8_26 5) A Multiobjective Memetic Algorithm for Multiobjective Unconstrained Binary Quadratic Programming Problem, by Ying Zhou, Lingjing Kong, Lijun Yan, Shaopeng Liu & Jiaming Hong, in Advances in Swarm Intelligence, https://link.springer.com/chapter/10.1007/978-3-030-78811-7_3 6) The Quadratic Unconstrained Binary Optimization Problem, by A.P. Punnen, editor, https://leeds-faculty.colorado.edu/glover/532-en_maevex_6020_panopto_datasheet_0.pdf https://www.mdpi.com/1999-4893/16/8/382 7) Handbook of Memetic Algorithms, F. Neri, C. Cotta and P. Moscato (Eds.), Springer, 2012. https://www.springer.com/gp/book/9783642232466 8) Memetic Algorithms for Business Analytics and Data Science: A Brief Survey, Pablo Moscato and Luke Mathieson, in Business and Consumer Analytics: New Ideas, Pablo Moscato and Natalie Jane de Vries (Eds), pp 545-608, https://link.springer.com/chapter/10.1007/978-3-030-06222-4_13
Student Requirements:	High-level programming skills, interest in scientific computing/machine learning/artificial intelligence. Experience in HPC and GPU computing, knowledge of symbolic regression and its applications is also a plus.
Programs:	This AO can be done under the following programs:   Program    Available To        SURF    Caltech students only  Click on a program name for program info and application requirements.