Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

We studied the quantum correlations between the nodes in a quantum neural network built of an array of quantum dots with dipole-dipole interaction. By means of the quasiadiabatic path integral simulation of the density matrix evolution in a presence of the common phonon bath we have shown the coherence in such system can survive up to the liquid nitrogen temperature of 77K and above. The quantum correlations between quantum dots are studied by means of calculation of the entanglement of formation in a pair of quantum dots with the typical dot size of a few nanometers and interdot distance of the same order. We have shown that the proposed quantum neural network can keep the mixture of entangled states of QD pairs up to the above mentioned high temperatures.

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“…and Ω = diag(0, δ, δ, 2δ). Some more details of the used numerical method have been recently presented in a conference paper [39].…”

Section: Dipole-dipole Interaction Of Quantum Dots In a Quantum Neura...mentioning

confidence: 99%

Entanglement in a quantum neural network based on quantum dots

Altaisky

Zol’nikova

Kaputkina

et al. 2017

Photonics and Nanostructures - Fundamentals and Applications

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“…This effect has been commonly termed in the literature as the emergence of the classical world [2][3][4][5][6][7][8]. Decoherence is ubiquitous in a myriad of systems and applications [9], e.g., quantum dots [10][11][12], quantum game theory [13,14], quantum walks [15,16], quantum information [17][18][19][20], two-level systems [21][22][23], cavities [24][25][26], ion trapping [27] or the spin-boson model [9,28]. Similarly different models have been proposed to study and quantify its effects on the coherence of quantum systems as well as to control them [29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Decoherence in quantum cavities: Environmental erasure of carpet-type structures

Honrubia,

Sanz

2021

Preprint

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The interaction with an environment provokes decoherence in quantum systems, which gradually suppresses their capability to display interference traits. Hence carpet-type patterns formed in quantum cavities constitute ideal systems to study the robustness of the underlying interference process against the harmful effects of decoherence. This fact is here numerically investigated by means of a simple dynamical model that captures the essential features of the phenomenon under Markovian conditions, thus leaving aside extra complications associated with a more detailed dynamical description of the system-environment interaction. More specifically, this model takes into account and reproduces the fact that decoherence effects are stronger as energy levels become more separated (in energy), which translates into a progressive collapse of the energy density matrix to its main diagonal. However, because energy dissipation is not considered, an analogous behavior is not observed in the position representation, where a proper spatial localization of the probability density does not take place, as one might expected, but a rather delocalized distribution. Furthermore, it is also shown that in this representation some off-diagonal correlations still persist, which requires the consideration of an additional spatial-type factor. This makes evident the rather complex nature of the decoherence phenomenon and hence the importance to have a good acquaintance with how it manifests in different representations with the purpose to determine and design reliable control mechanisms.I.

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“…Kak introduced the first quantum network that depends on neural network principles [ 9 ]. Since the first QNN model was introduced, various other models have been proposed [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Khalid

Ali

et al. 2020

Brain Sciences

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Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.

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Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

Cited by 5 publications

References 31 publications

Entanglement in a quantum neural network based on quantum dots

Entanglement in a quantum neural network based on quantum dots

Decoherence in quantum cavities: Environmental erasure of carpet-type structures

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

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