The simulation visualizes a small graph optimization problem in 3D, showing how QAOA's alternating cost and mixer layers reshape the probability of measuring each candidate solution as the circuit depth and angles change.
Pick a problem graph, drag the layer depth (p) slider to add or remove QAOA rounds, and press play to watch the classical optimizer tune the beta and gamma angles while the measurement probabilities evolve toward better cuts.
Problem graph select, layer depth (p) slider, angle optimizer play/pause, rebuild circuit
QAOA was proposed in 2014 by Edward Farhi, Jeffrey Goldstone, and Sam Gutmann, and as its layer count p approaches infinity, it mathematically converges to the earlier idea of adiabatic quantum computation.
The simulation visualizes a small graph optimization problem in 3D, showing how QAOA's alternating cost and mixer layers reshape the probability of measuring each candidate solution as the circuit depth and angles change.
The simulation visualizes a small graph optimization problem in 3D, showing how QAOA's alternating cost and mixer layers reshape the probability of measuring each candidate solution as the circuit depth and angles change.
Pick a problem graph, drag the layer depth (p) slider to add or remove QAOA rounds, and press play to watch the classical optimizer tune the beta and gamma angles while the measurement probabilities evolve toward better cuts.
QAOA was proposed in 2014 by Edward Farhi, Jeffrey Goldstone, and Sam Gutmann, and as its layer count p approaches infinity, it mathematically converges to the earlier idea of adiabatic quantum computation.