The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Neural and automata networks: dynamical behavior and applications
Neural and automata networks: dynamical behavior and applications
Sparse distributed memory and related models
Associative neural memories
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Information Sciences—Informatics and Computer Science: An International Journal
Quantum associative memory with distributed queries
Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
Quantum computation and quantum information
Quantum computation and quantum information
ICALP '98 Proceedings of the 25th International Colloquium on Automata, Languages and Programming
Tight bounds on quantum searching
Tight bounds on quantum searching
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
On the power of quantum computation
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
A Proposal of a Quantum Search Algorithm
ICCIT '09 Proceedings of the 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology
On the capacity of ternary Hebbian networks
IEEE Transactions on Information Theory
Lower bounds on the capacities of binary and ternary networks storing sparse random vectors
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
We describe a quantum algorithm for computing the intersection of two sets and its application to associative memory. The algorithm is based on a modification of Grover's quantum search algorithm (Grover, 1996). We present algorithms for pattern retrieval, pattern completion, and pattern correction. We show that the quantum associative memory can store an exponential number of memories and retrieve them in sub-exponential time. We prove that this model has advantages over known classical associative memories as well as previously proposed quantum models.