Benchmarking adaptative variational quantum algorithms on QUBO instances (Extended Abstract)

Adaptative Variational Quantum Algorithms (adapt-VQAs) are innovative algorithms that can dynamically adjust their circuit by adding and removing gates. While various adaptative methods have been proposed, a comprehensive comparison among them is still missing in the literature. This paper aims to fill this gap by benchmarking three adaptative algorithms against the fixed-structure QAOA. Our findings reveal that the adaptative methods generate circuits leading to solutions with approximation ratios comparable with QAOA, but use fewer gates. This leads to a decrease in computational time and an increased resilience to noise.