We investigate algorithmic approaches for hard optimization and learning problems, with an emphasis on measurable performance, constraints, and scientific relevance.
Research includes quantum approximate optimization, annealing, sampling, quantum-assisted machine learning, and rigorous comparisons with classical methods.