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Frontiers of Physics

Quantum connectivity optimization algorithms for entanglement source deployment in a quantum multi-hop network

Zhen-Zhen Zou, Xutao Yu, Zaichen Zhang

Published Sep 29, 2017• 13 citations
Publisher

Abstract

At first, the entanglement source deployment problem is studied in a quantum multi-hop network, which has a significant influence on quantum connectivity. Two optimization algorithms are introduced with limited entanglement sources in this paper. A deployment algorithm based on node position (DNP) improves connectivity by guaranteeing that all overlapping areas of the distribution ranges of the entanglement sources contain nodes. In addition, a deployment algorithm based on an improved genetic algorithm (DIGA) is implemented by dividing the region into grids. From the simulation results, DNP and DIGA improve quantum connectivity by 213.73% and 248.83% compared to random deployment, respectively, and the latter performs better in terms of connectivity. However, DNP is more flexible and adaptive to change, as it stops running when all nodes are covered.

DOI
10.1007/s11467-017-0721-7

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At first, the entanglement source deployment problem is studied in a quantum multi-hop network, which has a significant influence on quantum connectivity. Two optimization algorithms are introduced with limited entanglement sources in this paper. A deployment algorithm based on node position (DNP) improves connectivity by guaranteeing that all overlapping areas of the distribution ranges of the entanglement sources contain nodes. In addition, a deployment algorithm based on an improved genetic algorithm (DIGA) is implemented by dividing the region into grids. From the simulation results, DNP and DIGA improve quantum connectivity by 213.73% and 248.83% compared to random deployment, respectively, and the latter performs better in terms of connectivity. However, DNP is more flexible and adaptive to change, as it stops running when all nodes are covered.
A quantum network connecting quantum devices is expected to enhance quantum computing and information processing by increasing the number of qubits, and distributing computation and processing. However, noise-induced decoherence remains a big challenge as a quantum network can experience multiple sources of noises. Optimizing a quantum network for a given noise regime thus can improve its performance and reliability. This research uses multi-objective optimization with a genetic algorithm to find optimal conditions for constructing a quantum network in specific configurations. We employed the qwanta quantum network simulator to investigate two scenarios of a three-node quantum network with a quantum repeater in the middle. Pareto frontier analysis enables us to visualize the trade-offs between a quantum state fidelity and throughput in a quantum network. The results provide parameters that achieve the fidelity higher than the targeted value of 0.828 for quantum key distribution in a linear network with equal distances between nodes. However, for a three-nodes network with unequal distances based on Thailand's geographical locations, the highest fidelity achieved is 0.706. Expanding a genetic algorithm's search space to include a broader parameter range can potentially improve the results. This research demonstrates potential deployment of a genetic algorithm with multiple-objective optimization in a quantum network.
Quantum network is a promising platform for numerous quantum applications, including distributed quantum computing, secure communication, and improved sensing. Implementing entanglement distribution between remote quantum nodes plays a vital role in realizing the quantum network's capabilities, and it requires effective remote entanglement distribution protocols. To meet this requirement, we propose two strategies, namely connection-oriented and connectionless, to achieve end-to-end entanglement distribution inspired by the designs of classical communication. For the connection-oriented strategy, an exclusive quantum connection is pre-established between the source and destination nodes. For the connectionless strategy, entanglement swapping is performed hop-by-hop tentatively. In this way, the best-effort service is provided for the source node to establish long-distance entanglement with the destination node. Analogous to classical communication, the proposed strategies have their advantages and disadvantages. Our simulation results show that the connectionless strategy is more efficient and robust than the connection-oriented strategy in terms of throughput. However, the connection-oriented strategy provides reliable end-to-end entanglement distribution but at the expense of higher overhead. Moreover, the application scenarios of these two strategies are discussed. This work paves the road for designing end-to-end entanglement distribution protocols in quantum networks.
Quantum communication using qubits based on the principle of entangled photons is a promising solution to improve network security. However, it is difficult to successfully create an entanglement link or connection between two nodes, especially when they are far apart from each other. In addition, only one qubit can be exchanged over an established entanglement connection, resulting in a low throughput.In this paper, we propose Redundant Entanglement Pro-visioning and Selection (REPS) to maximize the throughput for multiple source-destination (SD) pairs in a circuit-switched, multi-hop quantum network. REPS has two distinct features: (i). It provisions backup resources for extra entanglement links between adjacent nodes for failure-tolerance; and (ii). It provides flexibility in selecting successfully created entanglement links to establish entanglement connections for the SD pairs to achieve network-wide optimization. Extensive analysis and simulations show that REPS can achieve optimal routing with a high probability, and improves the throughput by up to 68.35% over the highest-performing algorithms in existence. In addition, it also improves the fairness among the SD pairs in the networks.
Entanglement distribution networks are essential for enabling secure, reliable, and sustained communication through the future Quantum Internet. Multi-ring network topologies offer improved scalability and redundancy, but also introduce additional routing complexity for the task of efficiently distributing entanglement. This paper solves the problem of optimal entanglement distribution in multi-ring quantum network topologies. Two separate multi-ring network configurations are analyzed, one where requests can only be routed within a single ring layer, and involves a less complex node setup, and one where requests may switch ring layers when passing through the network's nodes, at the cost of additional complexity within the nodes themselves. We propose algorithms that solve the problem of entanglement distribution in terms of optimal time needed to satisfy a set of entanglement resupply requests with given network resources, as well as the problem of optimal resources in order to satisfy a set of entanglement resupply requests within a single entanglement transmission and measurement step. We provide a comprehensive analysis of the proposed algorithms through computer simulations, which indicate that the additional complexity of a network which allows switching between ring layers brings little benefit in terms of time to serve requests, but may reduce the number of resources needed to satisfy the entanglement distribution requests in a non-blocking manner.
Quantum Internet (QI) is a system of interconnected quantum computers able to exchange information encoded in the so called quantum bits (qubits). Differently from the classical counterpart, qubits benefit from a manifold properties guaranteed by quantum mechanics, such as superposition and entanglement. Despite the fact that quantum networks bring significant advantages, several phenomena can negatively impact the overall system, potentially hindering communication. In order to evaluate the network performance, a comprehensive probability expression is derived in this work to ultimately determine how many qubits are expected to be successfully received by nodes. On this basis, a Mixed-Integer Non-Linear Programming (MINLP) problem is formulated to fairly maximize the qubits exchanged between node pairs and jointly optimize (i) the position of the quantum source, and (ii) the entanglement distribution plan. To cope with the non-convexity of the problem, an iterative optimization algorithm, leveraging Block Coordinate Descendent (BCD) and Successive Convex Approximation (SCA) techniques, is proposed. A thorough simulation campaign is conducted to corroborate the theoretical findings. Numerical results demonstrates, under different parameter setups, that the proposed algorithm provides superior performance with respect to a baseline approach.
This paper investigates the application of quantum entanglement to optimize the structure of topological networks. Traditional topological network optimization algorithms often rely on static configurations, limiting their ability to leverage the inherent correlations within the network. We propose a novel algorithm that utilizes quantum entanglement to dynamically adjust network topology, resulting in enhanced performance. This research focuses on the fundamental principles of entanglement and its implications for network optimization. The core mechanism involves establishing entangled state representations of network nodes and exploring their interactions to guide the optimization process. We demonstrate the effectiveness of this approach through a simulation of a specific topological network, highlighting its ability to achieve significant improvements in network connectivity and overall performance compared to traditional methods.