Benchmarking a heuristic Floquet adiabatic algorithm for the Max-Cut problem

Understand

According to the adiabatic theorem of quantum mechanics, a system initially in the ground state of a Hamiltonian remains in the ground state if one slowly changes the Hamiltonian.

• This can be used in principle to solve hard problems on quantum computers.
• Generically, however, implementation of this Hamiltonian dynamics on digital quantum computers requires scaling Trotter step size with system size and simulation time, which incurs a large gate count.
• In this work, we argue that for classical optimization problems, the adiabatic evolution can be performed with a fixed, finite Trotter step.