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arXiv:1912.03265 · Entropy

Continuous Variables Graph States Shaped as Complex Networks: Optimization and Manipulation

Francesca Sansavini, Valentina Parigi

Published Dec 24, 2019• 14 citations
arXivHTMLPDF

Abstract

Complex networks structures have been extensively used for describing complex natural and technological systems, like the Internet or social networks. More recently, complex network theory has been applied to quantum systems, where complex network topologies may emerge in multiparty quantum states and quantum algorithms have been studied in complex graph structures. In this work, we study multimode Continuous Variables entangled states, named cluster states, where the entanglement structure is arranged in typical real-world complex networks shapes. Cluster states are a resource for measurement-based quantum information protocols, where the quality of a cluster is assessed in terms of the minimal amount of noise it introduces in the computation. We study optimal graph states that can be obtained with experimentally realistic quantum resources, when optimized via analytical procedure. We show that denser and regular graphs allow for better optimization. In the spirit of quantum routing, we also show the reshaping of entanglement connections in small networks via linear optics operations based on numerical optimization.

DOI
10.3390/e22010026

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Complex networks structures have been extensively used for describing complex natural and technological systems, like the Internet or social networks. More recently, complex network theory has been applied to quantum systems, where complex network topologies may emerge in multiparty quantum states and quantum algorithms have been studied in complex graph structures. In this work, we study multimode Continuous Variables entangled states, named cluster states, where the entanglement structure is arranged in typical real-world complex networks shapes. Cluster states are a resource for measurement-based quantum information protocols, where the quality of a cluster is assessed in terms of the minimal amount of noise it introduces in the computation. We study optimal graph states that can be obtained with experimentally realistic quantum resources, when optimized via analytical procedure. We show that denser and regular graphs allow for better optimization. In the spirit of quantum routing, we also show the reshaping of entanglement connections in small networks via linear optics operations based on numerical optimization.
Continuous variable cluster entangled states are a potential resource for universal quantum computation. Here we propose a scalable scheme to prepare a class of multimode cluster entangled states. In terms of graph states, all modes are denoted by the nodes, and the lines connecting the nodes represent the interactions between the connected nodes. Our cluster entangled states correspond to two-colourable graphs, in which the nodes belong to two different families, and the lines connect only the nodes of different families. The physical mechanism is attributed to quantum interference between multiple pathways for wave-mixing parametric interactions in near-resonant systems.
The effective routing of entanglement over a quantum network is a fundamental problem in quantum communication. The distribution and reshaping of multipartite entanglement over a quantum network requires scalable protocols that adapt to the underlying topology. Graph states provide a convenient framework for this challenging task, as they encode global entanglement in a structured way that can be locally manipulated to produce a variety of target states, without requiring additional long-distance quantum operations. In this article, we propose graph-state-based routing protocols for sharing GHZ states, achieving larger sizes than those achieved in the existing works for given network topologies. For this improvement, we consider tree structures connecting the users participating in the final GHZ states, as opposed to the linear configurations used in the earlier works. In particular, for grid networks, we show special constructions of such trees that achieve larger GHZ states than the prior works. Moreover, if the user nodes along which the entanglement is to be routed are pre-specified, a strategy is proposed to accomplish this routing.
Multimode entangled pure Gaussian states are an essential resource for continuous variable (CV) quantum information processing. Menicucci et al. [2011] have shown how to describe them as graphs with complex adjacency matrices. Among such complex graph states (CGSs), quantum optics experimental setups straightforwardly display correlations described by purely imaginary graph states (iRGSs) whereas theoretical literature favors off-diagonal real graph state (RGS) descriptions, which actually reflect the entanglement structure in the infinite squeezing limit [Gonzalez-Arciniegas et al. 2021]. We study them beyond this ideal case, thus establishing in the finite squeezing regime an equivalence criterion for the generic case of CGSs through a graph propagating set of conditions which either finds a Gaussian Local Unitary (GLU) converting one state to another or gives a proof of impossibility if such a GLU does not exist. In the experimentally relevant case of the conversion between iRGSs and RGSs, the set of conditions results in simple relations between the topologies of the equivalent real and imaginary graphs. Finally, we show that the canonical forms of iRGSs and RGSs imply that the topology of the CV real and imaginary graph states are too rigid to admit any exact qumode local-addition analogue to the qubit complementation or qudit local-addition, thus demonstrating the non-existence of any exact qumode local-addition for these states. We however define an approximate one when the squeezing is strong enough.
We demonstrate experimentally how to remove an arbitrary node from a continuous-variable cluster state and how to shorten any quantum wires of such a state. These two basic operations, performed in an unconditional fashion, are a manifestation of quantum erasure and can be employed to obtain various graph states from an initial cluster state. Starting with a sufficiently large cluster, the resulting graph states can then be used for universal quantum information processing. In the experiment, all variations of this cluster shaping are demonstrated on a four-mode linear cluster state through homodyne measurements and feedforward.
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.
We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of random graphs known as complex networks. In the case of classical random graphs, we derive an analytic expression for the averaged entanglement entropy while for general complex networks we rely on numerics. For a large number of nodes n we find a scaling where both the prefactor c and the sub-leading O (1) term g e are characteristic of the different classes of complex networks. In particular, g e encodes topological features of the graphs and is named network topological entropy. Our results suggest that quantum entanglement may provide a powerful tool for the analysis of large complex networks with non-trivial topological properties.