QAOA.jl: Toolkit for the Quantum and Mean-Field Approximate Optimization Algorithms
Tim Bode, Dmitry Bagrets, Aditi Misra-Spieldenner, Tobias Stollenwerk, Frank K. Wilhelm · The Journal of Open Source Software · 2023
Quantum algorithms are an area of intensive research thanks to their potential for speeding up certain specific tasks exponentially.However, for the time being, high error rates on the existing hardware realizations preclude the application of many algorithms that are based on the assumption of fault-tolerant quantum computation.On such noisy intermediatescale quantum (NISQ) devices (Preskill, 2018), the exploration of the potential of heuristic quantum algorithms has attracted much interest.A leading candidate for solving combinatorial optimization problems is the so-called Quantum Approximate Optimization Algorithm (QAOA) (Farhi et al., 2014).QAOA.jl is a Julia package (Bezanson et al., 2017) that implements the mean-field Approximate Optimization Algorithm (mean-field AOA) (Misra-Spieldenner et al., 2023) -a quantum-inspired classical algorithm derived from the QAOA via the mean-field approximation.This novel algorithm is useful in assisting the search for quantum advantage by providing a tool to discriminate (combinatorial) optimization problems that can be solved classically from those that cannot.Note that QAOA.jl has already been used during the research leading to Misra-Spieldenner et al. (2023).