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Daniel Lidar: Scaling Advantage in Approximate Optimization with Quantum Annealing

This talk will begin with a review of a decade of efforts to demonstrate a scaling advantage in optimization using quantum annealing. Exact optimization has proven to be an elusive target, but recent work has finally demonstrated a quantum scaling advantage in approximate optimization. Tailored quantum error suppression and correction methods play an important role in this demonstration. The advantage is achieved for a certain class of spin-glass problems, where, for sufficiently large optimality gaps, quantum annealing demonstrates a time-to-approximate solution that scales better than PT-ICM, the state-of-the-art classical method. This is joint work with Humberto Munoz-Bauza, arXiv:2401.07184