Entanglement in a quantum neural network based on quantum dots

Altaisky, M. V.; Zol’nikova, N. N.; Kaputkina, N. E.; Крылов, В. А.; Lozovik, Yu. E.; Dattani, Nikesh S.

doi:10.1016/j.photonics.2017.02.001

Cited by 14 publications

(15 citation statements)

References 51 publications

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“…One of the challenges in the design of quantum neurons is how to create a nonlinear activation function as quantum operations are required to be unitary and linear. There have been a number of publications that focus on creating quantum neural network [4][5][6][7]. One research has found that none of the proposals for quantum neural networks fully exploits both the advantages of quantum physics and computing in neural networks [8].…”

Section: Discussionmentioning

confidence: 99%

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Towards a Real Quantum Neuron

Hu¹

2018

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Google's AlphaGo represents the impressive performance of deep learning and the backbone of deep learning is the workhorse of highly versatile neural networks. Each network is made up of layers of interconnected neurons and the nonlinear activation function inside each neuron is one of the key factors that account for the unprecedented achievement of deep learning. Learning how to create quantum neural networks has been a long time pursuit since 1990's from many researchers, unfortunately without much success. The main challenge is to know how to design a nonlinear activation function inside the quantum neuron, because the laws in quantum mechanics require the operations on quantum neurons be unitary and linear. A recent discovery uses a special quantum circuit technique called repeat-until-success to make a nonlinear activation function inside a quantum neuron, which is the hard part of creating this neuron. However, the activation function used in that work is based on the periodic tangent function. Because of this periodicity, the input to this function has to be restricted to the range of [0, π/2), which is a serious constraint for its applications in real world problems. The function's periodicity also makes its neurons not suited for being trained with gradient descent as its derivatives oscillate. The purpose of our study is to propose a new nonlinear activation function that is not periodic so it can take any real numbers and its neurons can be trained with efficient gradient descent. Our quantum neuron offers the full benefit as a quantum entity to support superposition, entanglement, interference, while also enjoys the full benefit as a classical entity to take any real numbers as its input and can be trained with gradient descent. The performance of the quantum neurons with our new activation function is analyzed on IBM's 5Q quantum computer and IBM's quantum simulator.

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Section: Discussionmentioning

confidence: 99%

“…Since the early days of quantum computing, how to use it in the areas of machine learning to gain the quantum speed up has been a long time endeavor [4][5][6][7]. Neural networks can perform versatile learning tasks like clustering, classification, regression, pattern recognition, and more.…”

Section: Introductionmentioning

confidence: 99%

Towards a Real Quantum Neuron

Hu¹

2018

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“…Applying this operator in a state in superposition i |x i , 0 the value of x i will be calculated for all i in a single quantum operation, as described in Equation (9).…”

Section: Quantum Computingmentioning

confidence: 99%

“…Attempts to bring quantum computing power to a greater range of problems are the development of quantum machine-learning algorithms as decision trees [6], evolutionary algorithms [7] and artificial neural networks [8,9,10,11,12,13,14,15,16]. In this paper, we are concerned in the field of quantum weightless neural networks.…”

Section: Introductionmentioning

confidence: 99%

Weightless neural network parameters and architecture selection in a quantum computer

2016

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Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search.

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“…Quantum dots (QDs) have shown an important role in solar photovoltaic (PV), photocatalysts, photosensors and other optoelectronic components due to its quantum confinement effect, small particle size, large specific surface area and high catalytic activity . QDs can capture and transfer photogenerated electrons, accelerating the separation efficiency of photo‐induced carriers.…”

Section: Introductionmentioning

confidence: 99%

WO₃ Quantum Dots Decorated GO/Mg‐doped ZnO Composites for Enhanced Photocatalytic Activity under Nature Sunlight

Zhao

Fang

Chen

et al. 2018

Applied Organom Chemis

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Graphene oxide/Mg‐doped ZnO/tungsten oxide quantum dots composites (WQGOMZ) were prepared through co‐precipitation method, and were studied by X‐ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X‐ray photoelectron spectroscopy (XPS), Fluorescence spectra (FL), and UV–vis diffuse reflection spectra. Furthermore, the photocatalytic activity of resultant WQGOMZ was evaluated under nature sunlight. Experimental results showed that WO3QDs can remarkably heighten the photocatalytic activity of GOMZ composite, in which is nearly 6.58 times higher than that of GOMZ composite. Simultaneously, WQGOMZ composites possess optical memory ability and maintain high photocatalytic stability for more than 40 days. The enhanced photocatalytic activity and optical memory ability are attributed to the effective synergistic effect between ZnO and WO3QDs.

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Entanglement in a quantum neural network based on quantum dots

Cited by 14 publications

References 51 publications

Towards a Real Quantum Neuron

Towards a Real Quantum Neuron

Weightless neural network parameters and architecture selection in a quantum computer

WO₃ Quantum Dots Decorated GO/Mg‐doped ZnO Composites for Enhanced Photocatalytic Activity under Nature Sunlight

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Entanglement in a quantum neural network based on quantum dots

Cited by 14 publications

References 51 publications

Towards a Real Quantum Neuron

Towards a Real Quantum Neuron

Weightless neural network parameters and architecture selection in a quantum computer

WO3 Quantum Dots Decorated GO/Mg‐doped ZnO Composites for Enhanced Photocatalytic Activity under Nature Sunlight

Contact Info

Product

Resources

About

WO₃ Quantum Dots Decorated GO/Mg‐doped ZnO Composites for Enhanced Photocatalytic Activity under Nature Sunlight