QAOA Max-Cut Landscape Explorer

The Quantum Approximate Optimization Algorithm tackles Max-Cut by alternating a cost unitary and a mixer unitary, then classically tuning the two angles γ and β to maximize the expected number of cut edges. Instead of visualizing the circuit itself, this simulator renders the objective function E(γ,β) it is trying to maximize as a literal 3D terrain, built from a full statevector simulation on a small graph. Drag the angle sliders to move a marker across the surface and watch the live expectation value change, or launch a finite-difference gradient-ascent climb and watch it hunt for a peak the way a real hybrid quantum-classical training loop does — including getting stuck on a local optimum, the central practical difficulty of variational quantum algorithms.