Publication | Open Access
Resources for Bosonic Quantum Computational Advantage
49
Citations
46
References
2023
Year
Quantum ScienceBosonic ComputationsQuantum ComputingPhysicsEngineeringNatural SciencesQuantum Machine LearningQuantum Optimization AlgorithmQuantum Field TheoryQuantum SimulationQuantum InformationContinuous-variable Sampling ComputationsQuantum AlgorithmBosonic Quantum ComputationComputer ScienceQuantum EntanglementQuantum Error CorrectionQuantum Algorithms
Quantum computers promise to dramatically outperform their classical counterparts. However, the nonclassical resources enabling such computational advantages are challenging to pinpoint, as it is not a single resource but the subtle interplay of many that can be held responsible for these potential advantages. In this Letter, we show that every bosonic quantum computation can be recast into a continuous-variable sampling computation where all computational resources are contained in the input state. Using this reduction, we derive a general classical algorithm for the strong simulation of bosonic computations, whose complexity scales with the non-Gaussian stellar rank of both the input state and the measurement setup. We further study the conditions for an efficient classical simulation of the associated continuous-variable sampling computations and identify an operational notion of non-Gaussian entanglement based on the lack of passive separability, thus clarifying the interplay of bosonic quantum computational resources such as squeezing, non-Gaussianity, and entanglement.
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