Title :
Quantum associative memory with exponential capacity
Author :
Ventura, Dan ; Martinez, Tony
Author_Institution :
Dept. of Comput. Sci., Brigham Young Univ., Provo, UT, USA
Abstract :
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 by taking advantage of quantum parallelism. 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 covers necessary high-level quantum mechanical ideas and introduces a simple quantum associative memory. Furthermore, it provides discussion, empirical results and directions for future work
Keywords :
content-addressable storage; generalisation (artificial intelligence); neural nets; pattern classification; quantum theory; exponential capacity; generalisation; neural nets; pattern classification; quantum associative memory; quantum computation; quantum mechanics; quantum parallelism; quantum theory; Artificial neural networks; Associative memory; Computer networks; Computer science; Concurrent computing; Neurons; Polynomials; Quantum computing; Quantum mechanics; Wave functions;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.682319