Quantum Approximate Optimization Algorithm with Kerr resonators

The Quantum Approximate Optimization Algorithm (QAOA)---one of the leading algorithms for applications on intermediate-scale quantum processors---is designed to provide approximate solutions to combinatorial optimization problems with shallow quantum circuits. Here, we study QAOA implementations with Kerr resonators using qubits encoded into coherent states with opposite amplitudes. The dominant noise mechanism, i.e., photon losses, results in Z-biased noise with this encoding. We numerically show that running QAOA with Kerr qubits increases the approximation ratio for random instances of 8-qubit MaxCut with respect to "standard" qubits encoded into two-level systems, given the same average gate fidelities between the Kerr qubits and standard qubits.