The Quantum Approximate Optimization Algorithm (QAOA) is a popular iterative classical/quantum algorithms that uses an iterative approach with the two processors working together to find an optimized solution for a problem with a given set of inputs. Although the algorithm may not be too hard to program, it is much more challenging to use it and get an accurate solution.
Q-CTRL has introduced a new feature in their Python-based Fire Opal Error Mitigation software package that can automate and substantial improve the answer quality when running a QAOA program on an actual quantum computer. It simplifies the input process because a user only needs to input a graph and a goal, or a cost function rather than submit a quantum circuit. The QAOA solver will utilize the built-in features of Fire Opal, such as gate depth reduction, optimized gate placement, crosstalk elimination, control pulse optimization, measurement error mitigation, and other techniques to substantially improve the quality of the answers while reducing the number of steps. For the end user this will save both time and money.
For more information about this new QAOA Solver, you can view three documents posted on the Q-CTRL website. A blog post announcing it is available here, a documentation page can be found here, and a tutorial example of using it to solve a MaxCut problem can be seen here.
April 6, 2023