Published March 3, 2023 | Version Published + Supplemental Material
Journal Article Open

Resources for Bosonic Quantum Computational Advantage

  • 1. ROR icon PSL Research University
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon Kastler-Brossel Laboratory

Abstract

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.

Additional Information

© 2023 American Physical Society. We thank Frédéric Grosshans for inspiring discussions. This work was supported by the ANR JCJC project NoRdiC (ANR-21-CE47-0005) and Plan France 2030 project NISQ2LSQ (ANR-22-PETQ-0006). U. C. acknowledges funding provided by the Institute for Quantum Information and Matter, a NSF Physics Frontiers Center (NSF Grant No. PHY-1733907).

Attached Files

Published - PhysRevLett.130.090602.pdf

Supplemental Material - SM.pdf

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Additional details

Identifiers

Eprint ID
120668
Resolver ID
CaltechAUTHORS:20230404-414969100.16

Related works

Funding

Agence Nationale de la Recherche (ANR)
ANR-21-CE47-0005
Agence Nationale de la Recherche (ANR)
ANR-22-PETQ-0006
NSF
PHY-1733907

Dates

Created
2023-05-05
Created from EPrint's datestamp field
Updated
2023-05-05
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