A quantum-implementable neural network model

A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

[1]  Pablo A. Parrilo,et al.  Quantum algorithms for the ordered search problem via semidefinite programming , 2007 .

[2]  Carlo A. Trugenberger,et al.  High-Capacity Quantum Associative Memories , 2015, 1506.01231.

[3]  V.V. Shende,et al.  Synthesis of quantum-logic circuits , 2006, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[4]  Ashish Kapoor,et al.  Quantum deep learning , 2014, Quantum Inf. Comput..

[5]  F. Petruccione,et al.  An introduction to quantum machine learning , 2014, Contemporary Physics.

[6]  Maria Schuld,et al.  The quest for a Quantum Neural Network , 2014, Quantum Information Processing.

[7]  Li Fei,et al.  A study of quantum neural networks , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[8]  Seth Lloyd,et al.  Quantum algorithm for data fitting. , 2012, Physical review letters.

[9]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[10]  Tony R. Martinez,et al.  Quantum associative memory , 2000, Inf. Sci..

[11]  T. Spiller,et al.  Quantum computation by communication , 2005, quant-ph/0509202.

[12]  Weinberg,et al.  Precision tests of quantum mechanics. , 1989, Physical review letters.

[13]  L. Deng,et al.  The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web] , 2012, IEEE Signal Processing Magazine.

[14]  Shai Ben-David,et al.  Understanding Machine Learning: A Gentle Start , 2014 .

[15]  K. Pagiamtzis,et al.  Content-addressable memory (CAM) circuits and architectures: a tutorial and survey , 2006, IEEE Journal of Solid-State Circuits.

[16]  Lingli Wang,et al.  Programmable architecture for quantum computing , 2013 .

[17]  Elizabeth C. Behrman,et al.  Simulations of quantum neural networks , 2000, Inf. Sci..

[18]  Massimo Panella,et al.  Neural networks with quantum architecture and quantum learning , 2011, Int. J. Circuit Theory Appl..

[19]  R Raussendorf,et al.  A one-way quantum computer. , 2001, Physical review letters.

[20]  Robert RAUßENDORF.  MEASUREMENT-BASED QUANTUM COMPUTATION WITH CLUSTER STATES , 2009 .

[21]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[22]  Tamaryn Stable Ia Menneer,et al.  Quantum artificial neural networks , 1999 .

[23]  Maria Schuld,et al.  Simulating a perceptron on a quantum computer , 2014, ArXiv.

[24]  Peter W. Shor,et al.  Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[25]  S. Lloyd,et al.  Quantum algorithms for supervised and unsupervised machine learning , 2013, 1307.0411.

[26]  Vishal Sahni,et al.  Iris Data Classification Using Quantum Neural Networks , 2006 .

[27]  Teresa Bernarda Ludermir,et al.  Quantum perceptron over a field and neural network architecture selection in a quantum computer , 2016, Neural Networks.

[28]  G. Brassard,et al.  Quantum Amplitude Amplification and Estimation , 2000, quant-ph/0005055.

[29]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[30]  K. L. Brown.  Using the qubus for quantum computing , 2011 .

[31]  E. Farhi,et al.  A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.