Adiabatic Quantum Optimization for Associative Memory Recall
arXiv:1407.1904 · doi:10.3389/fphy.2014.00079
Abstract
Hopfield networks are a variant of associative memory that recall information stored in the couplings of an Ising model. Stored memories are fixed points for the network dynamics that correspond to energetic minima of the spin state. We formulate the recall of memories stored in a Hopfield network using energy minimization by adiabatic quantum optimization (AQO). Numerical simulations of the quantum dynamics allow us to quantify the AQO recall accuracy with respect to the number of stored memories and the noise in the input key. We also investigate AQO performance with respect to how memories are stored in the Ising model using different learning rules. Our results indicate that AQO performance varies strongly with learning rule due to the changes in the energy landscape. Consequently, learning rules offer indirect methods for investigating change to the computational complexity of the recall task and the computational efficiency of AQO.
22 pages, 11 figures. Updated for clarity and figures, to appear in Frontiers of Physics
References in corpus (9)
- Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm
- On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models
- The performance of the quantum adiabatic algorithm on random instances of two optimization problems on regular hypergraphs
- Bayesian Network Structure Learning Using Quantum Annealing
- A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems
- Training a Binary Classifier with the Quantum Adiabatic Algorithm
- Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization
- A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration
- Quantum pattern recognition with liquid-state nuclear magnetic resonance