Mixer-phaser Ansatze for quantum optimization with hard constraints

We introduce multiple parametrized circuit ansatze and present the results of a numerical study comparing their performance with a standard Quantum Alternating Operator Ansatz approach. The ansatze are inspired by mixing and phase separation in the QAOA, and also motivated by compilation considerations with the aim of running on near-term superconducting quantum processors. The methods are tested on random instances of a quadratic binary constrained optimization problem that is fully connected for which the space of feasible solutions has constant Hamming weight. For the parameter setting strategies and evaluation metric used, the average performance achieved by the QAOA is effectively matched by the one obtained by a mixer-phaser ansatz that can be compiled in less than half-depth of standard QAOA on most superconducting qubit processors.

Automated summary

We introduce multiple parametrized circuit ansatze and compare their performance with a standard Quantum Alternating Operator Ansatz approach. The ansatze are inspired by QAOA and compiled to run on near-term superconducting quantum processors. Experimental results show that a mixer-phaser ansatz can achieve similar performance as QAOA, with shorter depth, on most superconducting qubit processors.