A 64 bit quantum dragon data-set for machine learning

Abstract Design and examples of a sixty-four bit quantum dragon data-set are presented. A quantum dragon is a tight-binding model for a strongly disordered nanodevice, but when connected to appropriate semi-infinite leads has complete electron transmission for a finite interval of energies. The labeled data-set contains records which are quantum dragons, which are not quantum dragons, and which are indeterminate. The quantum dragon data-set is designed to be difficult for trained humans and machines to label a nanodevice with regard to its quantum dragon property. The 64 bit record length allows the data-set to be utilized in restricted Boltzmann machines which fit well onto the D-Wave 2000Q quantum annealer architecture.

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Equilibrium States and Thermodynamical Properties of D-Wave Paired HTSC in the Tight-Binding Model

physica status solidi (b) ◽

10.1002/1521-3951(200209)233:2<351::aid-pssb351>3.0.co;2-u ◽

2002 ◽

Vol 233 (2) ◽

pp. 351-363 ◽

Cited By ~ 10

Author(s):

R. Gonczarek ◽

M. G?adysiewicz ◽

M. Mulak

Keyword(s):

Tight Binding ◽

Equilibrium States ◽

Tight Binding Model ◽

Binding Model ◽

Thermodynamical Properties ◽

D Wave

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Tight-binding model in optical waveguides: Design principle and transferability for simulation of complex photonics networks

Physical Review A ◽

10.1103/physreva.104.023501 ◽

2021 ◽

Vol 104 (2) ◽

Author(s):

Yang Chen ◽

Xiaoman Chen ◽

Xifeng Ren ◽

Ming Gong ◽

Guang-can Guo

Keyword(s):

Optical Waveguides ◽

Design Principle ◽

Tight Binding ◽

Tight Binding Model ◽

Binding Model

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Intrinsic asymmetries in spin-valves: a tight binding model study

Journal of Magnetism and Magnetic Materials ◽

10.1016/j.jmmm.2003.08.027 ◽

2004 ◽

Vol 270 (3) ◽

pp. 298-304 ◽

Cited By ~ 1

Author(s):

Jisang Hong ◽

R.Q Wu ◽

R.B Muniz

Keyword(s):

Tight Binding ◽

Spin Valves ◽

Tight Binding Model ◽

Binding Model ◽

Model Study

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Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Scientific Reports ◽

10.1038/s41598-021-82197-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Guanglei Xu ◽

William S. Oates

Keyword(s):

Neural Network ◽

Maximum Likelihood ◽

Image Reconstruction ◽

Image Recognition ◽

Shannon Entropy ◽

Reconstruction Error ◽

Likelihood Method ◽

Restricted Boltzmann Machines ◽

Boltzmann Machines ◽

D Wave

AbstractRestricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ($$\beta $$ β ) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

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