Quantum Neural Network
1.1 Introduction and Background
The eld of articial neural networks (ANNs) draws its inspiration from the
working of human brain and the way brain processes information. An ANN
is a directed graph with highly interconnected nodes called neurons.Each
edge of the graph has a weight associated with it to model the synaptic
eciency. The training process involves updating the weights of the network
in such a way that the network learns to solve the problem.
The neurons in the network work together to solve specic problems.
The network can be trained to do various tasks like pattern recognition,
data classication,function approximation etc. ANNs are widely used in the
elds of computer vision and speech recognition.
1.1.1 Architecture of an Articial Neural Network
Learning is the way we acquire knowledge about the world around us, and it
is through this process of knowledge acquisition, that the environment alters
our behavioural responses. Similarly, in articial neural networks, learning
rules are used, to modify the behaviour of the network in response to the
external stimuli (inputs).
For multilayered feedforward networks, a commonly used algorithm for
weight adjustment is the backpropagation algorithm. There is some math
2 CHAPTER 1. QUANTUM NEURAL NETWORK
Figure 1.1: feedforward multilayer ANN
involved in the derivation of the formula which can be referred to from .
The nal form of the formula looks like:
wij = jyi
wij is the weight between the neurons i; j
j is the local gradient of the jth neuron
and, yi is the output of the ithneuron
1.2 Quantum Mechanics and ANN
There are problems that are computationally hard i.e. ...
... middle of paper ...
..., which are discussed in detail in .
1.3.3 Training and Performance
The training of the network can be carried out using the backpropagation
algorithm. Because of the unavailability of quantum hardware, the network
cannot be tested but we can simulate the network on a classical computer.
1.4 Summary and Discussion
Provide a summary and discuss what you have understood. summarize the
main points and also mention if have found some subtopics dicult.
 A. Narayanan, T. Menneer, Information Sciences 128 (2000) 231-255
 H. Everett, Relative state formulation of quantum mechanics, Reviews
of Modern Physics 29 (1957) 454-462.
 Satish Kumar, Neural Networks: A Classroom Approach
 T.S.I. Menneer, Quantum articial neural networks, Ph.D. Thesis, De-
partment of Computer Science, University of Exeter, Exeter, EX4 4PT,
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