Rigetti and Purdue University Demonstrate Quantum Preconditioning Framework for Constrained Optimization
Rigetti Computing and Purdue University have published joint research extending Rigetti's quantum preconditioning framework to hard-constrained combinatorial optimization problems. The method uses two-point variable correlations extracted from shallow QAOA circuits to modify the problem's objective function before it is passed to a classical solver.
OutlookPlausible
Within two years, this framework could become a standard preprocessing step in Rigetti's cloud service, letting users submit constrained optimization problems, receive QAOA-derived correlation data, and warm-start commercial classical solvers.