The Technology Innovation Institute (TII) in Abu Dhabi has developed a quantum optimization solver capable of addressing large-scale combinatorial optimization problems using only 17 qubits. The solver, detailed in a study published in Nature Communications, encodes 7,000 binary variables into a small number of qubits, significantly reducing computational overhead while maintaining high solution quality.
The hybrid quantum-classical algorithm leverages qubit correlations to maximize quantum resource efficiency, mitigating challenges like barren plateaus during model training. Tested on Maximum Cut problems, the solver achieved results comparable to or exceeding state-of-the-art classical methods, demonstrating its potential for applications in logistics, telecommunications, finance, and energy.
The research was conducted in collaboration with NVIDIA, Los Alamos National Laboratory, and Caltech, combining theoretical insights with experimental validation on commercially available quantum devices. TII’s Quantum Research Center plans to expand the solver’s applications and integrate it with classical algorithms for enhanced performance.
Read the full paper posted on the Nature Communications website here.
February 25, 2025
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