Adiabatic quantum optimization for associative memory recall
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.
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Quantum Computing Inst.
- Publication Date:
- OSTI Identifier:
- Grant/Contract Number:
- Accepted Manuscript
- Journal Name:
- Frontiers in Physics
- Additional Journal Information:
- Journal Volume: 2; Journal ID: ISSN 2296-424X
- Frontiers Research Foundation
- Research Org:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org:
- Country of Publication:
- United States
- 97 MATHEMATICS AND COMPUTING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; quantum computing; quantum information; hopfield; associative memory
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