A Quantum Associative Memory Based on Grover’s Algorithm

  • Dan Ventura
  • Tony Martinez


Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical ideas and introduces a quantum associative memory, a small version of which should be physically realizable in the near future.


Quantum Computation Associative Memory Quantum Algorithm Linear Superposition Bidirectional Associative Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 1999

Authors and Affiliations

  • Dan Ventura
    • 1
  • Tony Martinez
    • 1
  1. 1.Neural Networks and Machine Learning Laboratory Department of Computer ScienceBrigham Young UniversityProvoUSA

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