Adiabatic quantum optimization for associative memory recall

Seddiqi, Hadayat; Humble, Travis S.

doi:10.3389/fphy.2014.00079

Adiabatic quantum optimization for associative memory recall

Journal Article · Mon Dec 22 00:00:00 EST 2014 · Frontiers in Physics

DOI:https://doi.org/10.3389/fphy.2014.00079· OSTI ID:1185587

Seddiqi, Hadayat ^[1]; Humble, Travis S. ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Quantum Computing Inst.

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1185587

Journal Information:: Frontiers in Physics, Journal Name: Frontiers in Physics Vol. 2; ISSN 2296-424X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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