Optimization is a promising application for quantum computing; however, progress is limited by the time needed to score candidate solutions using quantum hardware. Here, the authors show that a small number of hardware measurements can train a classical model to predict scores, substantially reducing the number of quantum evaluations.
- Tom O’Leary
- Piotr Czarnik
- Lukasz Cincio