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arXiv:1806.07251 · NeuroQuantology

Decoherence in a Quantum Neural Network

Deniz Türkpençe, Tahir Çetin Akıncı, Serhat Şeker

Published May 7, 2018• 7 citations
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Abstract

In this study, we propose a spin-star model for spin-(1/2) particles in order to examine the coherence dynamics of a quantum neural network (QNN) unit. Since quantum computing paradigm promises advantages over their classical counterparts, quantum versions of neural networks can be examined in this context. We focus on quantum coherence as a natural resource for quantum computing and investigate the central spin coherence of a spin star model in the time domain in a dissipative environment. More particularly, we investigate the extend to which the central spin coherence time would be prolonged under specific parameters and spin-coupling Hamiltonians in a Markov environment. We show that Ising-type spin coupling is not desirable since it rapidly diminishes the coherence time in a dissipative environment.

DOI
10.14704/nq.2018.16.6.1181

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In this study, we propose a spin-star model for spin-(1/2) particles in order to examine the coherence dynamics of a quantum neural network (QNN) unit. Since quantum computing paradigm promises advantages over their classical counterparts, quantum versions of neural networks can be examined in this context. We focus on quantum coherence as a natural resource for quantum computing and investigate the central spin coherence of a spin star model in the time domain in a dissipative environment. More particularly, we investigate the extend to which the central spin coherence time would be prolonged under specific parameters and spin-coupling Hamiltonians in a Markov environment. We show that Ising-type spin coupling is not desirable since it rapidly diminishes the coherence time in a dissipative environment.
This study concerns with the dynamics of a quantum neural network unit in order to examine the suitability of simple neural computing tasks. More specifically, we examine the dynamics of an interacting spin model chosen as a candidate of a quantum perceptron for closed and open quantum systems. We adopt a collisional model enables examining both Markovian and non-Markovian dynamics of the proposed quantum system. We show that our quantum neural network (QNN) unit has a stable output quantum state in contact with an environment carrying information content. By the performed numerical simulations one can compare the dynamics in the presence and absence of quantum memory effects. We find that our QNN unit is suitable for implementing general neural computing tasks in contact with a Markovian information environment and quantum memory effects cause complications on the stability of the output state.
Abstract This study concerns with the evolution of entanglement in a quantum neural network (QNN) model that is locally in contact with data environments. As a valuable resource, duration of entanglement in quantum systems is extremely important. Therefore, the effect of various initial states on the occurrence or decay of entanglement are examined in the presence of information reservoirs. In this study, central spin model was investigated as a quantum version of neural networks inspired by biological models. The architecture of the model is based on a central spin system with two nodes where the nodes are coupled to independent spin baths. Numerical results show that initial state preparation has a profound effect on the fate of entanglement. The results show that the entanglement lifetime can be adjusted by engineering the reservoir states as well as the initial states of the system of interest. The results can be used to improve the performance of the formation or distribution of entanglement in realistic communication network states.
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.
This study is related to the fate of entanglement for initial separable states in a quantum neural network (QNN) model, which is in contact with the data environments locally. The duration of entanglement in quantum systems becomes extremely important when we consider it as a valuable resource. Thus, the effects of various initial states on the occurrence or decay of entanglement are investigated in the presence of information reservoirs. Especially in this study, central spin model has been examined as a quantum version of neural networks by taking inspiration from the biological models. Our model consists of a central spin system with two nodes to which the nodes are coupled to independent spin baths. Numerical results clearly show that different initial states have a profound effect on the fate of the entanglement. It also shows that the entanglement lifetime can be adjusted by regulating the reservoir states. The results can be used in realistic communication network situations to improve the performance of entanglement formation or distribution.
This study examines the disentanglement evolution of a quantum neural network (QNN) model locally in contact with data environments. As a valuable resource, duration of entanglement in quantum systems is extremely significant. Therefore, the effect of various initial states to the generation or decay of entanglement has been investigated under pure and maximally mixed environmental states as two limit cases. Numerical results show that initial state preparation has a profound impact on the fate of entanglement even in the course of maximally noisy environments. The obtained results reveal that the decay of entanglement of the quantum neural network (with flip-flop type interaction) is affected by the initial preparation of the Bell state even in the presence of pure state environmental monitoring. Depending on the results it’s also suggested to begin with coherent product states since it provides robust entanglement generation with longer disentanglement time during the open quantum system evolution.