|Ph.D Student||Salman Tamer|
|Subject||Quantum Neural Computation and Associative Memory|
|Department||Department of Computer Science||Supervisor||Professor Emeritus Yoram Baram|
|Full Thesis text|
This thesis presents quantum analogues of artificial neural networks. We analyze and compare their performance to known classical and previously proposed quantum models. First we propose a model for associative memory based on a modification of Grover’s quantum search algorithm and prove that the capacity of the model is exponential in the number of bits. We present algorithms for pattern completion and correction and prove that the model does not suffer from spurious memories and has a controllable basin of attraction. Then we define models of quantum neurons and devise an algorithm for Hebbian learning through quantum gates. We show that the algorithm implements probabilistic learning.