Journal articles: 'Quantum Networks'

1

Meng, Xiangyi, Xinqi Hu, Yu Tian, Gaogao Dong, Renaud Lambiotte, Jianxi Gao, and Shlomo Havlin. "Percolation Theories for Quantum Networks." Entropy 25, no. 11 (November 20, 2023): 1564. http://dx.doi.org/10.3390/e25111564.

Full text

Abstract:

Quantum networks have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. This review paper discusses a fundamental question: how can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect quantum network, where the connections are only partially entangled and subject to quantum noise? We survey recent studies addressing this issue by drawing exact or approximate mappings to percolation theory, a branch of statistical physics centered on network connectivity. Notably, we show that the classical percolation frameworks do not uniquely define the network’s indirect connectivity. This realization leads to the emergence of an alternative theory called “concurrence percolation”, which uncovers a previously unrecognized quantum advantage that emerges at large scales, suggesting that quantum networks are more resilient than initially assumed within classical percolation contexts, offering refreshing insights into future quantum network design.

APA, Harvard, Vancouver, ISO, and other styles

2

Miguel-Ramiro, Jorge, Alexander Pirker, and Wolfgang Dür. "Optimized Quantum Networks." Quantum 7 (February 9, 2023): 919. http://dx.doi.org/10.22331/q-2023-02-09-919.

Full text

Abstract:

The topology of classical networks is determined by physical links between nodes, and after a network request the links are used to establish the desired connections. Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests, which can substitute links and allow one to fulfill multiple network requests with the same resource state. We utilize this to design entanglement-based quantum networks tailored to their desired functionality, independent of the underlying physical structure. The kind of entanglement to be stored is chosen to fulfill all desired network requests (i.e. parallel bipartite or multipartite communications between specific nodes chosen from some finite set), but in such a way that the storage requirement is minimized. This can be accomplished by using multipartite entangled states shared between network nodes that can be transformed by local operations to different target states. We introduce a clustering algorithm to identify connected clusters in the network for a given desired functionality, i.e. the required network topology of the entanglement-based network, and a merging algorithm that constructs multipartite entangled resource states with reduced memory requirement to fulfill all desired network requests. This leads to a significant reduction in required time and resources, and provides a powerful tool to design quantum networks that is unique to entanglement-based networks.

APA, Harvard, Vancouver, ISO, and other styles

3

Xu, Zenglin. "Tensor Networks Meet Neural Networks." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2278/1/012003.

Full text

Abstract:

Abstract As a simulation of the human cognitive system, deep neural networks have achieved great success in many machine learning tasks and are the main driving force of the current development of artificial intelligence. On the other hand, tensor networks as an approximation of quantum many-body systems in quantum physics are applied to quantum physics, statistical physics, quantum chemistry and machine learning. This talk will first give a brief introduction to neural networks and tensor networks, and then discuss the cross-field research between deep neural networks and tensor networks, such as network compression and knowledge fusion, including our recent work on tensor neural networks. Finally, this talk will also discuss the connection to quantum machine learning.

APA, Harvard, Vancouver, ISO, and other styles

4

Zhang, Yulu, and Hua Lu. "Reliability Research on Quantum Neural Networks." Electronics 13, no. 8 (April 16, 2024): 1514. http://dx.doi.org/10.3390/electronics13081514.

Full text

Abstract:

Quantum neural networks (QNNs) leverage the strengths of both quantum computing and neural networks, offering solutions to challenges that are often beyond the reach of traditional neural networks. QNNs are being used in areas such as computer games, function approximation, and big data processing. Moreover, quantum neural network algorithms are finding utility in social network modeling, associative memory systems, and automatic control mechanisms. Nevertheless, ensuring the reliability of quantum neural networks is crucial as it directly influences network performance and stability. To investigate the reliability of quantum neural networks, this paper proposes a methodology wherein operator measurements are performed on the final states of the output quantum states of a quantum neural network. The proximity of these measurements to the target value is compared, and the fidelity value, combined with a quantum gate operation, is utilized to assess the reliability of the quantum neural network. Through network training, the results demonstrate that, under optimal parameters, both the fidelity of the final state measurement value and the target value of the model approach are approximately equal to 1. It indicates that training mitigates the errors stemming from encoding into the initial quantum state, thereby resulting in enhanced system reliability and accuracy.

APA, Harvard, Vancouver, ISO, and other styles

5

Mihály, András, and László Bacsárdi. "Optical transmittance based store and forward routing in satellite networks." Infocommunications journal 15, no. 2 (2023): 8–13. http://dx.doi.org/10.36244/icj.2023.2.2.

Full text

Abstract:

Quantum computing will play a crucial part in our security infrastructure for the coming years. Quantum networks can consist of direct optical fiber or free-space links. With the use of satellite channels, we can create a quantum network with higher coverage than using optical fibers where the distances are limited due to the properties of the fiber. One of the highest drivers of cost for satellite networks, apart from the cost of the technology needed for such systems, are the costs of launching and maintaining said satellites. By minimizing the satellites needed for a well-functioning quantum network, we can decrease said network’s cost, thus enabling a cheaper quantum internet. In this paper, we present an optical transmittance-based routing algorithm with which it is possible to conduct successful quantum entanglement transfer between terrestrial nodes.

APA, Harvard, Vancouver, ISO, and other styles

6

Zhang, Chang-Yue, Zhu-Jun Zheng, Shao-Ming Fei, and Mang Feng. "Dynamics of Quantum Networks in Noisy Environments." Entropy 25, no. 1 (January 12, 2023): 157. http://dx.doi.org/10.3390/e25010157.

Full text

Abstract:

Noise exists inherently in realistic quantum systems and affects the evolution of quantum systems. We investigate the dynamics of quantum networks in noisy environments by using the fidelity of the quantum evolved states and the classical percolation theory. We propose an analytical framework that allows us to characterize the stability of quantum networks in terms of quantum noises and network topologies. The calculation results of the framework determine the maximal time that quantum networks with different network topologies can maintain the ability to communicate under noise. We demonstrate the results of the framework through examples of specific graphs under amplitude damping and phase damping noises. We further consider the capacity of the quantum network in a noisy environment according to the proposed framework. The analytical framework helps us better understand the evolution time of a quantum network and provides a reference for designing large quantum networks.

APA, Harvard, Vancouver, ISO, and other styles

7

Curcic, Tatjana, Mark E. Filipkowski, Almadena Chtchelkanova, Philip A. D'Ambrosio, Stuart A. Wolf, Michael Foster, and Douglas Cochran. "Quantum networks." ACM SIGCOMM Computer Communication Review 34, no. 5 (October 15, 2004): 3–8. http://dx.doi.org/10.1145/1039111.1039117.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Hirche, Christoph. "Quantum Network Discrimination." Quantum 7 (July 25, 2023): 1064. http://dx.doi.org/10.22331/q-2023-07-25-1064.

Full text

Abstract:

Discrimination between objects, in particular quantum states, is one of the most fundamental tasks in (quantum) information theory. Recent years have seen significant progress towards extending the framework to point-to-point quantum channels. However, with technological progress the focus of the field is shifting to more complex structures: Quantum networks. In contrast to channels, networks allow for intermediate access points where information can be received, processed and reintroduced into the network. In this work we study the discrimination of quantum networks and its fundamental limitations. In particular when multiple uses of the network are at hand, the rooster of available strategies becomes increasingly complex. The simplest quantum network that capturers the structure of the problem is given by a quantum superchannel. We discuss the available classes of strategies when considering n copies of a superchannel and give fundamental bounds on the asymptotically achievable rates in an asymmetric discrimination setting. Furthermore, we discuss achievability, symmetric network discrimination, the strong converse exponent, generalization to arbitrary quantum networks and finally an application to an active version of the quantum illumination problem.

APA, Harvard, Vancouver, ISO, and other styles

9

Trahan, Corey, Mark Loveland, and Samuel Dent. "Quantum Physics-Informed Neural Networks." Entropy 26, no. 8 (July 30, 2024): 649. http://dx.doi.org/10.3390/e26080649.

Full text

Abstract:

In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed, and hybrid configurations are explored. The results show that (1) for some applications, quantum PINNs can obtain comparable accuracy with less neural network parameters than classical PINNs, and (2) adding quantum nodes in classical PINNs can increase model accuracy with less total network parameters for noiseless models.

APA, Harvard, Vancouver, ISO, and other styles

10

Franco, Mario, Octavio Zapata, David A. Rosenblueth, and Carlos Gershenson. "Random Networks with Quantum Boolean Functions." Mathematics 9, no. 8 (April 7, 2021): 792. http://dx.doi.org/10.3390/math9080792.

Full text

Abstract:

We propose quantum Boolean networks, which can be classified as deterministic reversible asynchronous Boolean networks. This model is based on the previously developed concept of quantum Boolean functions. A quantum Boolean network is a Boolean network where the functions associated with the nodes are quantum Boolean functions. We study some properties of this novel model and, using a quantum simulator, we study how the dynamics change in function of connectivity of the network and the set of operators we allow. For some configurations, this model resembles the behavior of reversible Boolean networks, while for other configurations a more complex dynamic can emerge. For example, cycles larger than 2N were observed. Additionally, using a scheme akin to one used previously with random Boolean networks, we computed the average entropy and complexity of the networks. As opposed to classic random Boolean networks, where “complex” dynamics are restricted mainly to a connectivity close to a phase transition, quantum Boolean networks can exhibit stable, complex, and unstable dynamics independently of their connectivity.

APA, Harvard, Vancouver, ISO, and other styles

11

Song, Junyang, Bo Lu, Lu Liu, and Chuan Wang. "Noisy Quantum Channel Characterization Using Quantum Neural Networks." Electronics 12, no. 11 (May 27, 2023): 2430. http://dx.doi.org/10.3390/electronics12112430.

Full text

Abstract:

Channel noise is considered to be the main obstacle in long-distance quantum communication and distributed quantum networks. Here, employing a quantum neural network, we present an efficient method to study the model and detect the noise of quantum channels. Based on various types of noisy quantum channel models, we construct the architecture of the quantum neural network and the model training process. Finally, we perform experiments to verify the training effectiveness of the scheme, and the results show that the cost function of the quantum neural network could approach above 90% of the channel model.

APA, Harvard, Vancouver, ISO, and other styles

12

García-Cobo, Iván. "Quantum Network Intelligent Management System." Optics 3, no. 4 (November 15, 2022): 430–37. http://dx.doi.org/10.3390/opt3040036.

Full text

Abstract:

Quantum network materializes the paradigm change caused by the depletion of classical computation. Quantum networks have been built gathering reliable quantum repeaters connected by optical fiber networks. The need to build robust and resilient networks against hacking attacks is fundamental in the design of the future quantum Internet, detecting structural security as the major issue in the current development of the technology. A network management method is proposed to achieve its real-time adaptation and to protect itself against sabotage or accidents that render part of the network or its nodes useless.

APA, Harvard, Vancouver, ISO, and other styles

13

Lohe, M. A. "Quantum synchronization over quantum networks." Journal of Physics A: Mathematical and Theoretical 43, no. 46 (October 27, 2010): 465301. http://dx.doi.org/10.1088/1751-8113/43/46/465301.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

A. Hussein, Shahad, and Alharith A. Abdullah. "Hybrid routing protocol for quantum network based on classical and quantum routing metrics." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (July 1, 2023): 197. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp197-204.

Full text

Abstract:

A quantum repeater is the heart of a quantum internet that enables end-to-end communication over long distances using the quantum entanglement feature. Although this characteristic gives quantum networks tremendous power in terms of speed and security, it puts quantum networks, especially the quantum internet, in front of challenges, like transforming a short-distance quantum link into a long-distance link, in addition to entanglement routing for finding the best path within a quantum network. So, this research aims to propose a hybrid quantum routing protocol (HQRP) based on routing metrics of the classical networks like hops count, as well as metrics of the quantum network represented by the possibility of entanglement between end nodes in the network. As a result, a mathematical model was built to determine the optimal path among many paths within the quantum network, and it was implemented on our quantum network simulator. Finally, we concluded that the proposed algorithm gives the optimal path based on the highest entanglement probability and lowest number of hop count.

APA, Harvard, Vancouver, ISO, and other styles

15

Buckley, Anita, Pavel Chuprikov, Rodrigo Otoni, Robert Soulé, Robert Rand, and Patrick Eugster. "An Algebraic Language for Specifying Quantum Networks." Proceedings of the ACM on Programming Languages 8, PLDI (June 20, 2024): 1313–35. http://dx.doi.org/10.1145/3656430.

Full text

Abstract:

Quantum networks connect quantum capable nodes in order to achieve capabilities that are impossible only using classical information. Their fundamental unit of communication is the Bell pair, which consists of two entangled quantum bits. Unfortunately, Bell pairs are fragile and difficult to transmit directly, necessitating a network of repeaters, along with software and hardware that can ensure the desired results. Challenging intrinsic features of quantum networks, such as dealing with resource competition, motivate formal reasoning about quantum network protocols. To this end, we developed BellKAT, a novel specification language for quantum networks based upon Kleene algebra. To cater to the specific needs of quantum networks, we designed an algebraic structure, called BellSKA, which we use as the basis of BellKAT's denotational semantics. BellKAT's constructs describe entanglement distribution rules that allow for modular specification. We give BellKAT a sound and complete equational theory, allowing us to verify network protocols. We provide a prototype tool to showcase the expressiveness of BellKAT and how to optimize and verify networks in practice.

APA, Harvard, Vancouver, ISO, and other styles

16

Qiu, Peng-Hui, Xiao-Guang Chen, and Yi-Wei Shi. "Solving Quantum Channel Discrimination Problem With Quantum Networks and Quantum Neural Networks." IEEE Access 7 (2019): 50214–22. http://dx.doi.org/10.1109/access.2019.2910840.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Kim, Hyunji, Kyungbae Jang, Sejin Lim, Yeajun Kang, Wonwoong Kim, and Hwajeong Seo. "Quantum Neural Network Based Distinguisher on SPECK-32/64." Sensors 23, no. 12 (June 18, 2023): 5683. http://dx.doi.org/10.3390/s23125683.

Full text

Abstract:

As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr’s work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing. Quantum neural networks can also learn and make predictions on data, just like classical neural networks. However, current quantum computers are constrained by many factors (e.g., the scale and execution time of available quantum computers), making it difficult for quantum neural networks to outperform classical neural networks. Quantum computers have higher performance and computational speed than classical computers, but this cannot be achieved in the current quantum computing environment. Nevertheless, it is very important to find areas where quantum neural networks work for technology development in the future. In this paper, we propose the first quantum neural network based distinguisher for the block cipher SPECK-32 in an NISQ. Our quantum neural distinguisher successfully operated for up to 5 rounds even under constrained conditions. As a result of our experiment, the classical neural distinguisher achieved an accuracy of 0.93, but our quantum neural distinguisher achieved an accuracy of 0.53 due to limitations in data, time, and parameters. Due to the constrained environment, it cannot exceed the performance of classical neural networks, but it can operate as a distinguisher because it has obtained an accuracy of 0.51 or higher. In addition, we performed an in-depth analysis of the quantum neural network’s various factors that affect the performance of the quantum neural distinguisher. As a result, it was confirmed that the embedding method, the number of the qubit, and quantum layers, etc., have an effect. It turns out that if a high-capacity network is needed, we have to properly tune properly to take into account the connectivity and complexity of the circuit, not just by adding quantum resources. In the future, if more quantum resources, data, and time become available, it is expected that an approach to achieve better performance can be designed by considering the various factors presented in this paper.

APA, Harvard, Vancouver, ISO, and other styles

18

Subhi, Doaa, and Laszlo Bacsardi. "Using Quantum Nodes Connected via the Quantum Cloud to Perform IoT Quantum Network." Condensed Matter 8, no. 1 (February 23, 2023): 24. http://dx.doi.org/10.3390/condmat8010024.

Full text

Abstract:

Computer networks consist of millions of nodes that need constant protection because of their continued vulnerability to attacks. Classical security methods for protecting such networks will not be effective enough if quantum computers become widespread. On the other hand, we can exploit the capabilities of quantum computing and communications to build a new quantum communication network. In this paper, we focused on enhancing the performance of the classical client–server Internet application. For this sake, we introduced a novel Internet of Things (IoT) quantum network that provides high security and Quality of Service (QoS) compared with the traditional IoT network. This can be achieved by adding quantum components to the traditional IoT network. Quantum counterpart nodes, channels, and servers are used. In order to establish a secure communication between the quantum nodes and the quantum server, we defined a new Communication Procedure (CP) for the suggested IoT quantum network. The currently available quantum computer has a small qubit size (from 50 to 433 qubits). The proposed IoT quantum network allows us to overcome this problem by concatenating the computation efforts of multiple quantum nodes (quantum processors).

APA, Harvard, Vancouver, ISO, and other styles

19

Laokondee, Suraphan, and Prabhas Chongstitvatana. "Quantum Neural Network model for Token allocation for Course Bidding." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 18, no. 1 (February 10, 2024): 112–18. http://dx.doi.org/10.37936/ecti-cit.2024181.247613.

Full text

Abstract:

Quantum computer has shown the advantage over the classical computer to solve some problems using the laws of quantum mechanics. With a combination of knowledge of machine learning and quantum computing, Quantum neural networks adapted the concept from classical neural networks and apply parameterized quantum gates as neural network weights. In this paper, we present an application of quantum neural networks with real-world data to predict token price used in a course bidding system. The experiments were carried out on the Qiskit quantum simulator. The result shows that quantum neural networks can achieve a good prediction result compared to the classical neural network. The best model configuration has the lowest RMSE 6.38%. This approach opens an opportunity to explore the benefit of quantum machine learning in many research fields in the future.

APA, Harvard, Vancouver, ISO, and other styles

20

Hirota, Yuichi, and Masaki Owari. "Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks." Applied Sciences 12, no. 12 (June 17, 2022): 6163. http://dx.doi.org/10.3390/app12126163.

Full text

Abstract:

Multicasting of quantum states is an essential feature of quantum internet. Since the noncloning theorem prohibits perfect cloning of an unknown quantum state, an appropriate protocol may depend on the purpose of the multicast. In this paper, we treat the multicasting of a single copy of an unknown state over a quantum network with free classical communication. We especially focus on protocols exactly multicasting an asymmetric optimal universal clone. Hence, these protocols are optimal and universal in terms of mean fidelity between input and output states, but the fidelities can depend on target nodes. Among these protocols, a protocol spending smaller communication resources is preferable. Here, we construct such a protocol attaining the min-cut of the network described as follows. Two (three) asymmetric optimal clones of an input state are created at a source node. Then, the state is divided into classical information and a compressed quantum state. The state is sent to two (three) target nodes using the quantum network coding. Finally, the asymmetric clones are reconstructed using LOCC with a small amount of entanglement shared among the target nodes and the classical information sent from the source node.

APA, Harvard, Vancouver, ISO, and other styles

21

Liang, Jianxiong, Xiaoguang Chen, and Tianyi Wang. "Percolation Distribution in Small-World Quantum Networks." Applied Sciences 12, no. 2 (January 11, 2022): 701. http://dx.doi.org/10.3390/app12020701.

Full text

Abstract:

Quantum networks have good prospects for applications in the future. Compared with classical networks, small-world quantum networks have some interesting properties. The topology of the network can be changed through entanglement exchange operations, and different network topologies will result in different percolation thresholds when performing entanglement percolation. A lower percolation threshold means that quantum networks require fewer minimum resources for communication. Since a shared singlet between two nodes can still be a limitation, concurrency percolation theory (ConPT) can be used to relax the condition. In this paper, we investigate how entanglement distribution is performed in small-world quantum networks to ensure that nodes in the network can communicate with each other by establishing communication links through entanglement swapping. Any node can perform entanglement swapping on only part of the connected edges, which can reduce the influence of each node in the network during entanglement swapping. In addition, the ConPT method is used to reduce the percolation threshold even further, thus obtaining a better network structure and reducing the resources required.

APA, Harvard, Vancouver, ISO, and other styles

22

SALIMI, S., R. RADGOHAR, and M. M. SOLTANZADEH. "SYMMETRY AND QUANTUM TRANSPORT ON NETWORKS." International Journal of Quantum Information 08, no. 08 (December 2010): 1323–35. http://dx.doi.org/10.1142/s0219749911006661.

Full text

Abstract:

We study the classical and quantum transport processes on some finite networks and model them by continuous-time random walks (CTRW) and continuous-time quantum walks (CTQW), respectively. We calculate the classical and quantum transition probabilities between two nodes of the network. We numerically show that there is a high probability to find the walker at the initial node for CTQWs on the underlying networks due to the interference phenomenon, even for long times. To get global information (independent of the starting node) about the transport efficiency, we average the return probability over all nodes of the network. We apply the decay rate and the asymptotic value of the average of the return probability to evaluate the transport efficiency. Our numerical results prove that the existence of the symmetry in the underlying networks makes quantum transport less efficient than the classical one. In addition, we find that the increasing of the symmetry of these networks decreases the efficiency of quantum transport on them.

APA, Harvard, Vancouver, ISO, and other styles

23

Zhang, Zhisheng, and Wenjie Gong. "Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7910971.

Full text

Abstract:

Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

APA, Harvard, Vancouver, ISO, and other styles

24

Rajan, Del. "Entropic DDoS Detection for Quantum Networks." Quantum Reports 4, no. 4 (December 13, 2022): 604–15. http://dx.doi.org/10.3390/quantum4040044.

Full text

Abstract:

Distributed Denial-of-Service (DDoS) attacks are a significant issue in classical networks. These attacks have been shown to impact the critical infrastructure of a nation, such as its major financial institutions. The possibility of DDoS attacks has also been identified for quantum networks. In this theoretical work, we introduce a quantum analogue of classical entropic DDoS detection systems and apply it in the context of detecting an attack on a quantum network. In particular, we examine DDoS attacks on a quantum repeater and harness the associated entanglement entropy for the detection system. Our results extend the applicability of quantum information from the domain of data security to the area of network security.

APA, Harvard, Vancouver, ISO, and other styles

25

Yang, Xin. "Quantum fuzzy neural network based on fuzzy number." Frontiers in Computing and Intelligent Systems 3, no. 2 (April 13, 2023): 99–105. http://dx.doi.org/10.54097/fcis.v3i2.7524.

Full text

Abstract:

Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, the cross-fusion of "fuzzy number theory + quantum neural network" is proposed to study the quantum fuzzy neural network (FQNN) based on fuzzy number. The Gaussian fuzzy function is used to generate the corresponding fuzzy affiliation matrix to describe the uncertain information in the data. The fuzzy independent variables are trained through the FQNN model, and the model is output after changing the parameters of the quantum forward propagation layer. Simulation experiments show that the quantum fuzzy neural network model based on fuzzy number is more efficient and accurate in this study compared with the quantum neural network model.

APA, Harvard, Vancouver, ISO, and other styles

26

Berec, Vesna. "Quantum networks: topology and spectral characterization." EPJ Web of Conferences 182 (2018): 02014. http://dx.doi.org/10.1051/epjconf/201818202014.

Full text

Abstract:

To utilize a scalable quantum network and perform a quantum state transfer within distant arbitrary nodes, coherence and control of the dynamics of couplings between the information units must be achieved as a prerequisite ingredient for quantum information processing within a hierarchical structure. Graph theoretic approach provides a powerful tool for the characterization of quantum networks with non-trivial clustering properties. By encoding the topological features of the underlying quantum graphs, relations between the quantum complexity measures are presented revealing the intricate links between a quantum and a classical networks dynamics.

APA, Harvard, Vancouver, ISO, and other styles

27

Jayakody, Mahesh N., Priodyuti Pradhan, Dana Ben Porath, and Eliahu Cohen. "Discrete-Time Quantum Walk on Multilayer Networks." Entropy 25, no. 12 (November 30, 2023): 1610. http://dx.doi.org/10.3390/e25121610.

Full text

Abstract:

A Multilayer network is a potent platform that paves the way for the study of the interactions among entities in various networks with multiple types of relationships. This study explores the dynamics of discrete-time quantum walks on a multilayer network. We derive a recurrence formula for the coefficients of the wave function of a quantum walker on an undirected graph with a finite number of nodes. By extending this formula to include extra layers, we develop a simulation model to describe the time evolution of the quantum walker on a multilayer network. The time-averaged probability and the return probability of the quantum walker are studied with Fourier, and Grover walks on multilayer networks. Furthermore, we analyze the impact of decoherence on quantum transport, shedding light on how environmental interactions may impact the behavior of quantum walkers on multilayer network structures.

APA, Harvard, Vancouver, ISO, and other styles

28

Alexander, Rafael N., Glen Evenbly, and Israel Klich. "Exact holographic tensor networks for the Motzkin spin chain." Quantum 5 (September 21, 2021): 546. http://dx.doi.org/10.22331/q-2021-09-21-546.

Full text

Abstract:

The study of low-dimensional quantum systems has proven to be a particularly fertile field for discovering novel types of quantum matter. When studied numerically, low-energy states of low-dimensional quantum systems are often approximated via a tensor-network description. The tensor network's utility in studying short range correlated states in 1D have been thoroughly investigated, with numerous examples where the treatment is essentially exact. Yet, despite the large number of works investigating these networks and their relations to physical models, examples of exact correspondence between the ground state of a quantum critical system and an appropriate scale-invariant tensor network have eluded us so far. Here we show that the features of the quantum-critical Motzkin model can be faithfully captured by an analytic tensor network that exactly represents the ground state of the physical Hamiltonian. In particular, our network offers a two-dimensional representation of this state by a correspondence between walks and a type of tiling of a square lattice. We discuss connections to renormalization and holography.

APA, Harvard, Vancouver, ISO, and other styles

29

Shang, Tao, Jiao Li, Zhuang Pei, and Jian-wei Liu. "Quantum network coding for general repeater networks." Quantum Information Processing 14, no. 9 (July 14, 2015): 3533–52. http://dx.doi.org/10.1007/s11128-015-1066-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Yang, Lihua, Xiaofei Qi, and Jinchuan Hou. "Quantum Nonlocality in Any Forked Tree-Shaped Network." Entropy 24, no. 5 (May 13, 2022): 691. http://dx.doi.org/10.3390/e24050691.

Full text

Abstract:

In the last decade, much attention has been focused on examining the nonlocality of various quantum networks, which are fundamental for long-distance quantum communications. In this paper, we consider the nonlocality of any forked tree-shaped network, where each node, respectively, shares arbitrary number of bipartite sources with other nodes in the next “layer”. The Bell-type inequalities for such quantum networks are obtained, which are, respectively, satisfied by all (tn−1)-local correlations and all local correlations, where tn denotes the total number of nodes in the network. The maximal quantum violations of these inequalities and the robustness to noise in these networks are also discussed. Our network can be seen as a generalization of some known quantum networks.

APA, Harvard, Vancouver, ISO, and other styles

31

Kumar Avtar, Dr Ram. "Entanglement Dynamics in Quantum Networks: Towards Scalable Quantum Information Processing." Journal of Quantum Science and Technology 1, no. 1 (March 31, 2024): 30–34. http://dx.doi.org/10.36676/jqst.v1.i1.07.

Full text

Abstract:

Entanglement dynamics plays a crucial role in the development of scalable quantum information processing architectures. Quantum networks, composed of interconnected quantum nodes, offer promising avenues for the distribution and manipulation of quantum information over long distances. In this paper, we investigate the dynamics of entanglement in quantum networks and explore strategies for achieving scalable quantum information processing. the generation, distribution, and preservation of entanglement in various network topologies and investigate the impact of noise and decoherence on entanglement dynamics. Furthermore, we discuss potential applications of entanglement in quantum communication, cryptography, and computation, highlighting the importance of understanding and controlling entanglement dynamics for realizing practical quantum technologies. Through theoretical analysis and numerical simulations, we provide insights into the challenges and opportunities associated with entanglement dynamics in quantum networks, paving the way towards scalable quantum information processing architectures.

APA, Harvard, Vancouver, ISO, and other styles

32

Sineglazov, Victor, and Petro Chynnyk. "Quantum Convolution Neural Network." Electronics and Control Systems 2, no. 76 (June 23, 2023): 40–45. http://dx.doi.org/10.18372/1990-5548.76.17667.

Full text

Abstract:

In this work, quantum convolutional neural networks are considered in the task of recognizing handwritten digits. A proprietary quantum scheme for the convolutional layer of a quantum convolutional neural network is proposed. A proprietary quantum scheme for the pooling layer of a quantum convolutional neural network is proposed. The results of learning quantum convolutional neural networks are analyzed. The built models were compared and the best one was selected based on the accuracy, recall, precision and f1-score metrics. A comparative analysis was made with the classic convolutional neural network based on accuracy, recall, precision and f1-score metrics. The object of the study is the task of recognizing numbers. The subject of research is convolutional neural network, quantum convolutional neural network. The result of this work can be applied in the further research of quantum computing in the tasks of artificial intelligence.

APA, Harvard, Vancouver, ISO, and other styles

33

Jorswieck, Eduard, and Bo Rong. "Quantum Communication Networks." IEEE Wireless Communications 29, no. 1 (February 2022): 11. http://dx.doi.org/10.1109/mwc.2022.9749200.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Wiersma, D. S. "Random Quantum Networks." Science 327, no. 5971 (March 11, 2010): 1333–34. http://dx.doi.org/10.1126/science.1187084.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Cabello, Adán, Lars Eirik Danielsen, Antonio J. López-Tarrida, and José R. Portillo. "Quantum social networks." Journal of Physics A: Mathematical and Theoretical 45, no. 28 (June 27, 2012): 285101. http://dx.doi.org/10.1088/1751-8113/45/28/285101.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Lewenstein, Maciej, and Mariusz Olko. "‘Quantum’ neural networks." Network: Computation in Neural Systems 2, no. 2 (January 1991): 207–30. http://dx.doi.org/10.1088/0954-898x_2_2_005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Perseguers, S., M. Lewenstein, A. Acín, and J. I. Cirac. "Quantum random networks." Nature Physics 6, no. 7 (May 16, 2010): 539–43. http://dx.doi.org/10.1038/nphys1665.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Gupta, Sanjay, and R. K. P. Zia. "Quantum Neural Networks." Journal of Computer and System Sciences 63, no. 3 (November 2001): 355–83. http://dx.doi.org/10.1006/jcss.2001.1769.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Slagle, Kevin. "Quantum Gauge Networks: A New Kind of Tensor Network." Quantum 7 (September 14, 2023): 1113. http://dx.doi.org/10.22331/q-2023-09-14-1113.

Full text

Abstract:

Although tensor networks are powerful tools for simulating low-dimensional quantum physics, tensor network algorithms are very computationally costly in higher spatial dimensions. We introduce quantum gauge networks: a different kind of tensor network ansatz for which the computation cost of simulations does not explicitly increase for larger spatial dimensions. We take inspiration from the gauge picture of quantum dynamics, which consists of a local wavefunction for each patch of space, with neighboring patches related by unitary connections. A quantum gauge network (QGN) has a similar structure, except the Hilbert space dimensions of the local wavefunctions and connections are truncated. We describe how a QGN can be obtained from a generic wavefunction or matrix product state (MPS). All 2k-point correlation functions of any wavefunction for M many operators can be encoded exactly by a QGN with bond dimension O(Mk). In comparison, for just k=1, an exponentially larger bond dimension of 2M/6 is generically required for an MPS of qubits. We provide a simple QGN algorithm for approximate simulations of quantum dynamics in any spatial dimension. The approximate dynamics can achieve exact energy conservation for time-independent Hamiltonians, and spatial symmetries can also be maintained exactly. We benchmark the algorithm by simulating the quantum quench of fermionic Hamiltonians in up to three spatial dimensions.

APA, Harvard, Vancouver, ISO, and other styles

40

Zheng, Yun, Chonghao Zhai, Dajian Liu, Jun Mao, Xiaojiong Chen, Tianxiang Dai, Jieshan Huang, et al. "Multichip multidimensional quantum networks with entanglement retrievability." Science 381, no. 6654 (July 14, 2023): 221–26. http://dx.doi.org/10.1126/science.adg9210.

Full text

Abstract:

Quantum networks provide the framework for quantum communication, clock synchronization, distributed quantum computing, and sensing. Implementing large-scale and practical quantum networks relies on the development of scalable architecture and integrated hardware that can coherently interconnect many remote quantum nodes by sharing multidimensional entanglement through complex-medium quantum channels. We demonstrate a multichip multidimensional quantum entanglement network based on mass-manufacturable integrated-nanophotonic quantum node chips fabricated on a silicon wafer by means of complementary metal-oxide-semiconductor processes. Using hybrid multiplexing, we show that multiple multidimensional entangled states can be distributed across multiple chips connected by few-mode fibers. We developed a technique that can efficiently retrieve multidimensional entanglement in complex-medium quantum channels, which is important for practical uses. Our work demonstrates the enabling capabilities of realizing large-scale practical chip-based quantum entanglement networks.

APA, Harvard, Vancouver, ISO, and other styles

41

Lodahl, Peter. "Quantum-dot based photonic quantum networks." Quantum Science and Technology 3, no. 1 (October 25, 2017): 013001. http://dx.doi.org/10.1088/2058-9565/aa91bb.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Kim, Ilki, and Günter Mahler. "Quantum chaos in small quantum networks." Journal of Modern Optics 47, no. 2-3 (February 2000): 177–86. http://dx.doi.org/10.1080/09500340008244035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Dey, Indrakshi, Nicola Marchetti, Marcello Caleffi, and Angela Sara Cacciapuoti. "Quantum Game Theory Meets Quantum Networks." IEEE Wireless Communications 31, no. 4 (August 2024): 90–96. http://dx.doi.org/10.1109/mwc.001.2300288.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Kashif, Muhammad, and Saif Al-Kuwari. "Design Space Exploration of Hybrid Quantum–Classical Neural Networks." Electronics 10, no. 23 (November 30, 2021): 2980. http://dx.doi.org/10.3390/electronics10232980.

Full text

Abstract:

The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybrid quantum–classical neural network (HQCNN) architectures. Following our proposed methodology, we develop different variants of hybrid neural networks and compare them with pure classical architectures of equivalent size. Finally, we empirically evaluate our proposed hybrid variants and show that the addition of quantum layers does provide a noticeable computational advantage.

APA, Harvard, Vancouver, ISO, and other styles

45

Nguyen, Hung Viet, Zunaira Babar, Dimitrios Alanis, Panagiotis Botsinis, Daryus Chandra, Mohd Azri Mohd Izhar, Soon Xin Ng, and Lajos Hanzo. "Towards the Quantum Internet: Generalised Quantum Network Coding for Large-Scale Quantum Communication Networks." IEEE Access 5 (2017): 17288–308. http://dx.doi.org/10.1109/access.2017.2738781.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Sharma, Purva, Kwonhue Choi, Ondrej Krejcar, Pavel Blazek, Vimal Bhatia, and Shashi Prakash. "Securing Optical Networks Using Quantum-Secured Blockchain: An Overview." Sensors 23, no. 3 (January 20, 2023): 1228. http://dx.doi.org/10.3390/s23031228.

Full text

Abstract:

The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.

APA, Harvard, Vancouver, ISO, and other styles

47

Shukla, Manish Kumar, Minyi Huang, Indranil Chakrabarty, and Junde Wu. "Correlations in Quantum Network Topologies Created with Cloning." Mathematics 11, no. 11 (May 25, 2023): 2440. http://dx.doi.org/10.3390/math11112440.

Full text

Abstract:

With progress in quantum technologies, the field of quantum networks has emerged as an important area of research. In the last few years, there has been substantial progress in understanding the correlations present in quantum networks. In this article, we study cloning as a prospective method to generate three party quantum networks which will help us to create larger networks. We analyze various quantum network topologies that can be created using cloning transformations. This would be useful in situations wherever the availability of entangled pairs is limited. In addition to that, we focus on the problem of distinguishing networks created by cloning from those that are created by distributing independently generated entangled pairs. We find that there are several states that cannot be distinguished using the Finner inequalities in the standard way. For such states, we propose an extension to the existing Finner inequality for triangle networks by further increasing the number of observers from three to four or six depending on the network topology. This takes into account the additional correlations that exist in the case of cloned networks. In the last part of the article, we use tripartite mutual information to distinguish cloned networks from networks created by independent sources and further use squashed entanglement as a measure to quantify the amount of dependence in the cloned networks.

APA, Harvard, Vancouver, ISO, and other styles

48

Khalid, Mustafa, Jun Wu, Taghreed M. Ali, Thaair Ameen, Ahmed A. Moustafa, Qiuguo Zhu, and Rong Xiong. "Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms." Brain Sciences 10, no. 7 (July 7, 2020): 431. http://dx.doi.org/10.3390/brainsci10070431.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

49

Landman, Jonas, Natansh Mathur, Yun Yvonna Li, Martin Strahm, Skander Kazdaghli, Anupam Prakash, and Iordanis Kerenidis. "Quantum Methods for Neural Networks and Application to Medical Image Classification." Quantum 6 (December 22, 2022): 881. http://dx.doi.org/10.22331/q-2022-12-22-881.

Full text

Abstract:

Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known training algorithms. The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation for inference and training of classical neural networks. We then present extensive experiments applied to medical image classification tasks using current state of the art quantum hardware, where we compare different quantum methods with classical ones, on both real quantum hardware and simulators. Our results show that quantum and classical neural networks generates similar level of accuracy, supporting the promise that quantum methods can be useful in solving visual tasks, given the advent of better quantum hardware.

APA, Harvard, Vancouver, ISO, and other styles

50

Lee, Yuan, Wenhan Dai, Don Towsley, and Dirk Englund. "Quantum network utility: A framework for benchmarking quantum networks." Proceedings of the National Academy of Sciences 121, no. 17 (April 19, 2024). http://dx.doi.org/10.1073/pnas.2314103121.

Full text

Abstract:

The central aim of quantum networks is to facilitate user connectivity via quantum channels, but there is an open need for benchmarking metrics to compare diverse quantum networks. Here, we propose a general framework for quantifying the performance of a quantum network by estimating the value created by connecting users through quantum channels. In this framework, we define the quantum network utility metric U QN to capture the social and economic value of quantum networks. The proposed framework accommodates a variety of applications from secure communications to distributed sensing. As a case study, we investigate the example of distributed quantum computing in detail. We determine the scaling laws of quantum network utility, which suggest that distributed edge quantum computing has more potential for success than its classical equivalent. We believe the proposed utility-based framework will serve as a foundation for guiding and assessing the development of quantum network technologies and designs.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Quantum Networks'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles