QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis
The Quantum Approximate Optimization Algorithm (QAOA) is a promising variational algorithm for solving combinatorial optimiza tion problems on near-term devices. However, as the number of layers in a QAOA circuit increases, which is correlated with the quality of the solution, the number of parameters to optimize grows linearly. This results in more iterations required by the classical opti mizer, which results in an increasing computational burden as more circuit executions are needed. To mitigate this issue, we introduce QAOA-PCA, a novel reparameterization technique that employs Principal Component Analysis (PCA) to reduce the dimensionality of the QAOA parameter space. By extracting principal components from optimized parameters of smaller problem instances, QAOA PCA facilitates efficient optimization with fewer parameters on larger instances. Our empirical evaluation on the prominent Max Cut problem demonstrates that QAOA-PCA consistently requires fewer iterations than standard QAOA, achieving substantial effi ciency gains. While this comes at the cost of a slight reduction in approximation ratio compared to QAOA with the same number of layers, QAOA-PCA almost always outperforms standard QAOA when matched by parameter count. QAOA-PCA strikes a favorable balance between efficiency and performance, reducing optimization overhead without significantly compromising solution quality.
Tue 17 JunDisplayed time zone: Athens change
11:45 - 12:30 | |||
11:45 15mPaper | QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis E-QSE | ||
12:00 15mPaper | A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection E-QSE A: Antonio Trovato University of Salerno, A: Martin Beseda , A: Dario Di Nucci University of Salerno | ||
12:15 15mPaper | Simulation of Cybersecurity Attacks on Hybrid Quantum Systems E-QSE A: Vita Santa Barletta University of Bari, A: Danilo Caivano University of Bari, A: Miriana Calvano University of Bari, A: Antonio Curci University of Bari, A: Antonio Lopopolo , A: Antonio Piccinno University of Bari, Italy |