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Applications of quantum inspired computational intelligence: a survey

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Abstract

This paper makes an exhaustive survey of various applications of Quantum inspired computational intelligence (QCI) techniques proposed till date. Definition, categorization and motivation for QCI techniques are stated clearly. Major Drawbacks and challenges are discussed. The significance of this work is that it presents an overview on applications of QCI in solving various problems in engineering, which will be very much useful for researchers on Quantum computing in exploring this upcoming and young discipline.

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Abbreviations

ANFIS:

Adaptive neuron-fuzzy inference system

AQM:

Adaptive quantum mutation

ASVR:

Adaptive support vector regression

ACO:

Ant colony optimization

AF:

Artificial fish

AFSA:

Artificial fish swarm algorithm

AI:

Artificial intelligence

BP:

Back propagation

BPNN:

Back-propagation NN

BWGC:

BPNN-weighted grey-C3LSP

CS:

Clonal selection

CGPQGA:

Coarse-grained parallel QGA

CI:

Computational intelligence

CET:

Contemporary evolution target

DE:

Differential evolution

DPSO:

Discrete binary version of PSO

EDAs:

Estimation of distribution algorithms

EA:

Evolutionary algorithms

EC:

Evolutionary computation

FQN:

Feedback QN

FCM:

Fuzzy C-means

FS:

Fuzzy system

GA:

Genetic algorithm

GD:

Gradient descent

HNN:

Hamiltonian NN

Hop.NN:

Hopfield NN

ICA:

Immune clonal algorithm

IS:

Immune systems

IMSCQGA:

Improved mutative scale chaos QGA

iPNN:

Integrative probabilistic evolving spiking NNs

IS:

Intelligent systems

K-SOFM:

Kohonen’s self-organizing feature map

LSQET:

Logarithmic search with quantum existence testing

MP:

Matching pursuit

ME:

Multi-granularity evolution

MLP:

Multilayer perceptron

NN:

Neural network

NQASVR:

Neuromorphic quantum-based adaptive support vector regression

NGARCH:

Nonlinear generalized autoregressive conditional heteroscedasticity

NQGA:

Novel QGA

PSO:

Particle swarm optimization

pSNM:

Probabilistic spiking neuron model

QoS:

Quality of service

QAM:

Quantum associative memory

Q bit:

Quantum bit

QBP:

Quantum BP

QCEA:

Quantum clone EA

Qu.Cl:

Quantum clustering

QCBPN:

Quantum complex-valued BP neuron

Qcomputer:

Quantum computer

QC:

Quantum computing

QEP:

Quantum evolutionary programming

QFSM:

Quantum finite state machines

Qgate:

Quantum gate

QACO:

Quantum inspired ACO

QAFSA:

Quantum Inspired artificial fish swarm algorithm

QCI:

Quantum inspired computational intelligence

QEA:

Quantum inspired EA

QGA:

Quantum inspired genetic algorithm

QNN:

Quantum inspired neural network

QPSO:

Quantum inspired PSO

QM:

Quantum mechanics

QMin.:

Quantum minimization

QN:

Quantum neuron

QDE:

Quantum-inspired DE

QEAs:

Quantum-inspired evolutionary algorithms

QISOM:

Quantum-inspired self-organizing map

QiSNN:

Quantum-inspired spiking NN

QT-BPNN:

Quantum-tuned BPNN

RBFNN:

Radial basis function NN

RQGA:

Real-coded QGA

RNN:

Recurrent NN

SOM:

Self-organizing map

SVMs:

Support vector machines

SA:

Swarm algorithm

vQEA:

Versatile QEA

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  1. Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    A. Manju & M. J. Nigam

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  1. A. Manju
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  2. M. J. Nigam
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Correspondence to A. Manju.

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Manju, A., Nigam, M.J. Applications of quantum inspired computational intelligence: a survey. Artif Intell Rev 42, 79–156 (2014). https://doi.org/10.1007/s10462-012-9330-6

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  • DOI: https://doi.org/10.1007/s10462-012-9330-6

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