Speaker
Description
In recent years, approaches inspired by fundamental physics have played an important role in the development of modern machine learning algorithms, from energy based models to diffusion processes. Motivated by this perspective, we propose a hybrid quantum-classical learning framework inspired by adiabatic quantum dynamics and quantum annealing for solving constraint satisfaction problems.In this work, we focus on 3-SAT instances represented as graphs. The training objective combines energy minimization of the 3-SAT Hamiltonian with a residual term derived from McLachlan’s variational principle, encouraging the learned dynamics to approximate an adiabatic path within the accessible variational manifold.Our approach offers a framework for studying how ideas from quantum annealing and variational quantum dynamics can be used to design learning algorithms for NP-complete constraint satisfaction problem. Beyond computational applications, this perspective may also be relevant for fundamental physics, where constraint satisfaction, frustrated energy landscapes and adiabatic state preparation naturally arise. Potential applications include the variational preparation of ground states of many-body quantum systems, the study of lattice gauge theories and constrained Hilbert spaces and the characterization of frustrated spin models and glassy energy landscapes.