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| Home > Publications database > QAOA.jl: Toolkit for the Quantum and Mean-Field Approximate Optimization Algorithms |
| Journal Article | FZJ-2023-03042 |
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2023
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Please use a persistent id in citations: doi:10.21105/joss.05364 doi:10.34734/FZJ-2023-03042
Abstract: Quantum algorithms are an area of intensive research thanks to their potential for speedingup certain specific tasks exponentially. However, for the time being, high error rates on theexisting hardware realizations preclude the application of many algorithms that are basedon the assumption of fault-tolerant quantum computation. On such noisy intermediate-scale quantum (NISQ) devices (Preskill, 2018), the exploration of the potential of heuristicquantum algorithms has attracted much interest. A leading candidate for solving combinatorialoptimization 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 Ap-proximate Optimization Algorithm (mean-field AOA) (Misra-Spieldenner et al., 2023) - aquantum-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 atool to discriminate (combinatorial) optimization problems that can be solved classically fromthose that cannot. Note that QAOA.jl has already been used during the research leading toMisra-Spieldenner et al. (2023).Additionally, QAOA.jl also implements the QAOA efficiently to support the extensive classicalsimulations typically required in research on the topic. The corresponding parameterizedcircuits are based on Yao.jl (Luo et al., 2020, 2023) and Zygote.jl (Innes et al., 2019, 2023),making it both fast and automatically differentiable, thus enabling gradient-based optimization.A number of common optimization problems such as MaxCut, the minimum vertex-coverproblem, the Sherrington-Kirkpatrick model, and the partition problem are pre-implemented tofacilitate scientific benchmarking.
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