Graph states are a class of important multiparty entangled quantum states, of which Bell pairs are the special case. Realizing a robust and fast distribution of arbitrary graph states in the downstream layer of the quantum network is essential for enabling large-scale quantum networks. To address this, we propose a novel quantum network protocol, called P2PGSD, inspired by the classical Peer-to-Peer network. This protocol efficiently implements general graph state distribution in the network layer, demonstrating significant advantages in resource efficiency and scalability, particularly for sparse graph states. An explicit mathematical model for the general graph state distribution problem has also been constructed, above which the intractability for a wide class of resource minimization problems is proved and the optimality of the existing algorithms is discussed. Moreover, we leverage the space-time quantum network for memory management in network challenges, drawing inspiration from special relativity. We suggest a universal quantum distributed computation framework to exploit the strengths of our protocols, as confirmed by numerical simulations that reveal up to a 50% enhancement for general sparse graph states. This work marks a significant step toward resource-efficient multiparty entanglement distribution for diverse network topologies.
Graph states are a class of important multiparty entangled quantum states, of which Bell pairs are the special case. Realizing a robust and fast distribution of arbitrary graph states in the downstream layer of the quantum network is essential for enabling large-scale quantum networks. To address this, we propose a novel quantum network protocol, called P2PGSD, inspired by the classical Peer-to-Peer network. This protocol efficiently implements general graph state distribution in the network layer, demonstrating significant advantages in resource efficiency and scalability, particularly for sparse graph states. An explicit mathematical model for the general graph state distribution problem has also been constructed, above which the intractability for a wide class of resource minimization problems is proved and the optimality of the existing algorithms is discussed. Moreover, we leverage the space-time quantum network for memory management in network challenges, drawing inspiration from special relativity. We suggest a universal quantum distributed computation framework to exploit the strengths of our protocols, as confirmed by numerical simulations that reveal up to a 50% enhancement for general sparse graph states. This work marks a significant step toward resource-efficient multiparty entanglement distribution for diverse network topologies.
Graph states are a class of important multiparty entangled quantum states, of which Bell pairs are the special case. Realizing a robust and fast distribution of arbitrary graph states in the downstream layer of the quantum network is essential for enabling large-scale quantum networks. To address this, we propose a novel quantum network protocol, called P2PGSD, inspired by the classical Peer-to-Peer network. This protocol efficiently implements general graph state distribution in the network layer, demonstrating significant advantages in resource efficiency and scalability, particularly for sparse graph states. An explicit mathematical model for the general graph state distribution problem has also been constructed, above which the intractability for a wide class of resource minimization problems is proved and the optimality of the existing algorithms is discussed. Moreover, we leverage the space-time quantum network for memory management in network challenges, drawing inspiration from special relativity. We suggest a universal quantum distributed computation framework to exploit the strengths of our protocols, as confirmed by numerical simulations that reveal up to a 50% enhancement for general sparse graph states. This work marks a significant step toward resource-efficient multiparty entanglement distribution for diverse network topologies.
Graph states are a class of important multiparty entangled quantum states, of which Bell pairs are the special case. Realizing a robust and fast distribution of arbitrary graph states in the downstream layer of the quantum network is essential for enabling large-scale quantum networks. To address this, we propose a novel quantum network protocol, called P2PGSD, inspired by the classical Peer-to-Peer network. This protocol efficiently implements general graph state distribution in the network layer, demonstrating significant advantages in resource efficiency and scalability, particularly for sparse graph states. An explicit mathematical model for the general graph state distribution problem has also been constructed, above which the intractability for a wide class of resource minimization problems is proved and the optimality of the existing algorithms is discussed. Moreover, we leverage the space-time quantum network for memory management in network challenges, drawing inspiration from special relativity. We suggest a universal quantum distributed computation framework to exploit the strengths of our protocols, as confirmed by numerical simulations that reveal up to a 50% enhancement for general sparse graph states. This work marks a significant step toward resource-efficient multiparty entanglement distribution for diverse network topologies.
Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum networks can help address this challenge by enabling the construction of large, robust, and more capable quantum computing platforms by connecting smaller quantum computers. Moreover, unlike classical systems, quantum networks can enable fully secured long-distance communication. Thus, quantum networks lie at the heart of the success of future quantum information technologies. In quantum networks, multipartite entangled states distributed over the network help implement and support many quantum network applications for communications, sensing, and computing. Our work focuses on developing optimal techniques to generate and distribute multipartite entanglement states efficiently. Prior works on generating general multipartite entanglement states have focused on the objective of minimizing the number of maximally entangled pairs while ignoring the heterogeneity of the network nodes and links as well as the stochastic nature of underlying processes. In this work, we develop a hypergraph-based linear programming framework that delivers optimal (under certain assumptions) generation schemes for general multipartite entanglement represented by graph states, under the network resources, decoherence, and fidelity constraints, while considering the stochasticity of the underlying processes. We illustrate our technique by developing generation schemes for the special cases of path and tree graph states and discuss optimized generation schemes for more general classes of graph states. Using extensive simulations over a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform prior known schemes by up to orders of magnitude.
The quantum internet will extend today's networks with a capability no classical infrastructure can offer: the distribution of entanglement between distant nodes. That single resource underlies services provably beyond classical reach, such as secure communication against computationally unbounded adversaries, blind delegated computation on remote quantum servers, and entangled networks of atomic clocks. Most of these services require a single multipartite state shared among several nodes at once — for the most part a graph state: one qubit per vertex of a graph, one entangling interaction per edge. This Thesis addresses the distribution of one such state: given an arbitrary network of entanglement switches, deliver the m-qubit path graph state, in the order the application requests, to m single-qubit end nodes — the weakest client model in the literature. Prior protocols support arbitrary topologies or such minimal end nodes, never both. The strongest of them are also centralized: the construction and the memory demand concentrate at a single node. The Thesis takes the opposite approach: a distributed protocol. The Path Graph State Distribution (PathGSD) protocol grows the target state one end node at a time behind a single reference qubit that walks a classically planned route, the shortest ordered Steiner walk. The work is partitioned among the switches, which coordinate only through entanglement and classical messages; no single node ever builds or stores the whole state. The design is deliberately simple, guided by engineering principles, and the simplicity pays at scale: planning is polynomial and completes in seconds on a network of 22,500 switches, where the closest optimization-based alternatives face exponential blow-up. Correctness is proved in the graph-state measurement calculus; a memory-reuse discipline and a parallel multi-domain execution mode complete the protocol. The protocol is implemented as communicating discrete-event processes on the QuantumSavory simulator, validated against exact theoretical expectations, and evaluated against the strongest available baselines: the star-specific Piecemaker and Factory protocols, simulated under noise, and the Graph State Transfer (GST) algorithm's proved cost expressions, evaluated exactly on arbitrary topologies. PathGSD consumes fewer Einstein-Podolsky-Rosen (EPR) pairs than the exact GST baseline on every single-domain family tested, keeps switch memory constant in the network size where the centralized baseline's grows, matches the star-specific protocols on their home topology, and reaches measured speed-ups of up to 1.9× when two administrative domains execute in parallel.
Graph states are a particular class of entangled quantum states, that play essential roles in distributed quantum computing and quantum communication. In both cases, graph states are usually used as resources for a specific set of quantum computers to share information. Therefore, distributing a graph state across a quantum network is an important problem. Currently, we do not have an efficient method for generating Einstein-Podolsky-Rosen(EPR) pairs on demand. Hence EPR pairs needed to distribute graph states are critical resources, and it is important to design distribution algorithms that reduce the need for such resources. Meignant et al. [1] presented the edge-decorated complete graph algorithm that uses n(n – 1)/2 number of EPR pairs to distribute the given graph state, where n is number of node in the network. Fischer et al [2] proposed the Graph State Transfer (GST) algorithm, which consumes fewer EPR pairs. However, GST requires an unbounded amount of memory at each node and preparation of the entire graph state in a single network node. In this poster, we present an integer programming formulation that removes these two assumptions. The model accounts for both the topology of the quantum network and memory constraints at each network node. Meanwhile, we propose a greedy algorithm to solve the problem.
Multipartite entangled states are great resources for quantum networks. In this work, we study the distribution, or routing, of entangled states over fixed, but arbitrary, physical networks. Our simplified model represents each use of a quantum channel as the sharing of a Bell pair; local operations and classical communications are considered to be free. We introduce two protocols to distribute, respectively, Greenberger-Horne-Zeilinger (GHZ) states and arbitrary graph states over arbitrary quantum networks. The GHZ states' distribution protocol takes a single step and is optimal in terms of the number of Bell pairs used; the graph states' distribution protocol uses, at most, twice as many Bell pairs and steps as the optimal routing protocol for the worst-case scenario.