Title: Pauli Correlation Encoding for Scalable Quantum Optimization: Analysis, constraints, and use cases<\/p>\n\n\n\n
Speakers: Vicente P. Soloviev (Principal Researcher) and Jacobo Padin Martinez (Quantum Computing Scientist)<\/p>\n\n\n\n
Abstract: Advances in quantum computing are opening new opportunities for solving complex optimization problems that challenge classical methods, especially in finance. This webinar presents two complementary lines of research that push the boundaries of quantum optimization in realistic settings. First, we show how a gate\u2011based variational quantum algorithm can be applied to large\u2011scale portfolio optimization by assigning multiple assets to each qubit using Pauli Correlation Encoding (PCE). This enables the treatment of portfolios with more than 250 variables\u2014far beyond the limits of conventional one\u2011hot encodings\u2014by clustering highly correlated assets and optimizing them through a hybrid quantum\u2013classical workflow.<\/p>\n\n\n\n
Building on this, we explore how PCE can be extended to general constrained combinatorial optimization problems. While standard PCE struggles to enforce constraints reliably, we introduce an Iterative\u2011PCE method that significantly improves solution quality and constraint satisfaction across a wide range of problem sizes.<\/p>\n\n\n\n
Together, these results demonstrate both the challenges and the promise of PCE\u2011based techniques, highlighting scalable strategies for applying gate\u2011based quantum algorithms to meaningful real\u2011world optimization tasks in the NISQ era.<\/p>\n\n\n\n