Skip to main content
Scientific Reports logoLink to Scientific Reports
. 2020 Dec 28;10:22390. doi: 10.1038/s41598-020-78960-5

Resource prioritization and balancing for the quantum internet

Laszlo Gyongyosi 1,2,, Sandor Imre 1
PMCID: PMC7770047  PMID: 33372180

Abstract

The quantum Internet enables networking based on the fundamentals of quantum mechanics. Here, methods and procedures of resource prioritization and resource balancing are defined for the quantum Internet. We define a model for resource consumption optimization in quantum repeaters, and a strongly-entangled network structure for resource balancing. We study the resource-balancing efficiency of the strongly-entangled structure. We prove that a strongly-entangled quantum network is two times more efficient in a resource balancing problem than a full-mesh network of the traditional Internet.

Subject terms: Mathematics and computing, Computer science, Pure mathematics

Introduction

The quantum Internet130 aims to provide an adequate answer to the computational power that becomes available with quantum computers3160. To provide a seamless transition to the legal users from the traditional Internet to the quantum Internet, the creation of advanced services and methods for the quantum Internet are emerging tasks5254,6167. The quantum Internet is modeled as a quantum network consisting of quantum repeaters and entangled connections between the quantum repeaters2,66129. This entangled quantum network forms a general framework for the quantum Internet, enabling long-distance quantum communications, multi-hop entanglement and multi-hop QKD (quantum key distribution)25, utilization of quantum protocols, advanced distributed computing, high-precision sensor networks, and the establishment of a global-scale quantum Internet.

A crucial problem related to the quantum Internet is the resource optimization of the quantum repeaters and the handling of resource requirement issues such as non-servable resource requests in the quantum repeaters1826. These fundamental questions are still open and have not been addressed for the quantum Internet.

Here, we define methods for resource prioritization and resource balancing for the quantum Internet. The aim of the proposed solutions is to optimize the resource allocation mechanisms and to reduce the resource consumption of the network entities of the quantum Internet. A model of resource consumption130134 of quantum repeaters is proposed, and its optimization is realized through the weightings of the entanglement throughputs of the entangled connections of the quantum repeaters. We also propose a method for optimizing the entanglement swapping procedure and determine the conditions of deadlock-free entanglement swapping. For resource balancing, a strongly-entangled network structure is defined. This network is modeled as an independent entity in the quantum Internet, composed of an arbitrary number of quantum repeaters such that all quantum repeaters are entangled with each other. The primary aim of the strongly-entangled structure is to serve those quantum nodes that have non-servable resource requests due to resource issues or an arbitrary network issue; these quantum nodes are referred to as low-priority quantum nodes.

The strongly-entangled structure injects additional resources into the quantum network to manage the resource issues of an arbitrary number of low-priority quantum nodes. The structure also provides optimized resource balancing for the low-priority quantum nodes. We prove the resource-balancing efficiency of the strongly-entangled structure and study its fault tolerance. We show that a strongly-entangled quantum network structure, due to the advanced attributes of quantum networking, is two times more efficient in resource balancing than a classical full-mesh135,136 network structure.

The novel contributions of our manuscript are as follows:

  1. We define methods and procedures for resource prioritization and resource balancing in the quantum Internet.

  2. The resource prioritization covers the resource consumption optimization of the quantum repeaters via the entanglement throughput weightings, prioritization of entanglement swapping in the quantum repeaters, and deadlock-free entanglement swapping.

  3. A strongly-entangled structure is defined for an optimal resource balancing. We prove the resource-balancing efficiency of the proposed structure and prove its fault tolerance. We show that a strongly-entangled quantum network structure is two times more efficient in resource balancing than a classical full-mesh network structure.

This paper is organized as follows. In “Preliminaries” section, preliminaries are summarized. In “Method” section, methods for resource consumption optimization are defined. “Strongly-entangled structure for resource balancing in the quantum internet” section proposes a solution for optimal resource balancing. A performance analysis is given in “Performance evaluation” section. Finally, “Conclusions” section provides the conclusions. Supplementary information is included in the Appendix.

Preliminaries

Basic terms

Entanglement fidelity

The aim of the entanglement distribution procedure is to establish a d-dimensional entangled system between the distant points A and B, through the intermediate quantum repeater nodes. Let d=2, and let β00=1200+11 be the entangled state subject to be established between distant points A and B. At a particular two-partite state σ established between A and B, the fidelity of σ is evaluated as

F=β00|σ|β00. 1

Without loss of generality, an aim of a practical entanglement distribution is to reach F0.9824,12,68,69,137,138.

Entangled network structure

Let V refer to the nodes of an entangled quantum network N, which consists of a transmitter node AV, a receiver node BV, and quantum repeater nodes RiV, i=1,,q. Let E=Ej, j=1,,m refer to a set of edges (an edge refers to an entangled connection in a graph representation) between the nodes of V, where each Ej identifies an Ll-level entanglement, l=1,,r, between quantum nodes xj and yj of edge Ej, respectively. Let N=V,S be an actual quantum network with V nodes and a set S of entangled connections. An Ll-level, l=1,,r, entangled connection ELlx,y, refers to the shared entanglement between a source node x and a target node y, with hop-distance

dx,yLl=2l-1, 2

since the entanglement swapping (extension) procedure doubles the span of the entangled pair in each step. This architecture is also referred to as the doubling architecture2,68,69,138.

For a particular Ll-level entangled connection ELlx,y with hop-distance (2), there are dx,yLl-1 intermediate nodes between the quantum nodes x and y.

Entanglement throughput

Let BF(ELli) refer to the entanglement throughput of a given Ll entangled connection ELli measured in the number of d-dimensional entangled states established over ELli per sec at a particular fidelity F (dimension of a qubit system is d=2)24,12,68,69,137,138.

For any entangled connection ELli, a condition c should be satisfied, as

c:BF(ELli)BF*(ELli),fori, 3

where BF*(ELli) is a critical lower bound on the entanglement throughput at a particular fidelity F of a given ELli, i.e., BF(ELli) of a particular ELli has to be at least BF*(ELli).

Oscillator cycles

To quantify the entanglement throughput of the entangled connections, time is measured in number of cycles C. The time tC of a cycle C is determined by an oscillator unit OC that is available for all the entities of the quantum network, such that tC=1/fC, where fC is the frequency of OC, with fC=1/tC.

Definitions

Resource consumption of a quantum repeater

Let αRi,Llk be the resource consumption of quantum repeater Ri associated with a k-th entangled connection Llk, k=1,,z, where l is the level of entanglement of the connection and z is the total number of entangled connections of Ri.

Let ΥRi,Llk be the resource consumption of quantum repeater Ri associated with the quantum memory usage at Llk; let ϕRi,Llk be the resource consumption of quantum repeater Ri associated with the entanglement purification of Llk; let τRi,Llk be the resource consumption of quantum repeater Ri associated with the entanglement distribution to a target node B; and let νRi,Llk be the resource consumption of quantum repeater Ri associated with the entanglement swapping US of Llk. Then, αRi,Llk can be defined as

αRi,Llk:=BFLlkRi,Llk+ζRi,Llk+CRi,Llk, 4

where the term Ri,Llk is defined as

Ri,Llk:=ΥRi,Llk+ϕRi,Llk+τRi,Llk+νRi,Llk, 5

where BFLlk is the entanglement throughput (Bell states per C) of the entangled connection Llk, while ζRi,Llk identifies the resource consumption of quantum repeater Ri associated with the path selection, and CRi,Llk refers to the cost of auxiliary classical communications.

Set of outcoming entangled states

Let ρA be an input entangled density matrix (i.e., half pair of an entangled state) in quantum repeater Ri, and let AρA be the set of possible r outcoming entangled states in Ri,

AρA:=σB,1,,σB,r, 6

where σB,i is the i-th possible outcoming density matrix. The set AρA is therefore identifies those (purified) entangled states, that can be selected for the US entanglement swapping with ρA to formulate an extended entangled connection via Ri.

Extended entangled connection

Using (6), an extended entangled connection is depicted as

LlRsβρA,RdβσB, 7

where βρA identifies subsystem ρA of the entangled state βAB, βσB identifies subsystem σB of the entangled state βAB, RsβρA is the Rs source quantum node with βρA, while RdβσB is the Rd destination quantum node with βσB, where βσB is selected from set AρA for the entanglement swapping to formulate βAB.

Set of destination quantum nodes

The set AρA in (6) is determined for a particular incoming density ρA by the set DRi of Rd destination quantum nodes that share an entangled connection with a current quantum repeater Ri, as

DRi:=RdβσB,1,,RdβσB,r, 8

where RdβσB,i refers to the Rd destination quantum node with βσB,i, i=1,,r.

Set of entangled connections via swapping

Let SPRi,ρA refer to the set of entangled connections that contains the entangled connection that is resulted between distant source Rs and destination Rd via an entanglement swapping in a particular quantum repeater Ri using input state ρA and output state σB, as

SPRi,ρA:=SPσB,RsβρA,RdβσBSPRi,RdβσB, 9

where SPRi,RdσB is the set of paths that pass through Ri using the entangled pair βσB in Rd.

Strongly-entangled structure

For a SN strongly-entangled structure, the number of low-priority quantum nodes (quantum nodes with non-servable resource requests) in N is nc, while SN is the number of quantum repeaters in a SN strongly-entangled structure, R1SN,,RSNSN. Since SN is strongly-entangled, each quantum repeater in SN has SN-1 entangled connections, and the ESN number of entangled connections within SN is

ESN:=SN·SN-12. 10

The entanglement levels of the ESN entangled connections in SN are defined in the following manner. Let A be the ingress node of SN, and let B be the egress node of SN, with hop-distance dA,B. Then, the Ld(x,y) entangled connections in function of the dx,y) hop-distance between quantum nodes x,ySN in the SN strongly-entangled structure are distributed as follows:

L(dx,y):=L1=SN-1=dA,BL2=L1-1LSN-2=LSN-3-1LSN-1=LdA,B=1, 11

at a particular number SN of quantum nodes. (Note, the strongly-entangled structure utilizes different entanglement levels than the doubling architecture, therefore in (11) the entanglement levels are denoted in different manner.)

Capability of a strongly-entangled structure

Assuming that there is a set Snc of nc low-priority Ri quantum repeaters in the network, i=1,,nc, the RqSN in SN is associated with entanglement throughput request (Bell states per C)

BRqSN,Snc:=1SNi=1ncBRi, 12

while for the internal entangled connections

QRqSN,RzSN:=1SN2i=1ncBRi. 13

where RzSN is a neighbor of RqSN in SN, zq, q=1,,SN.

Since, by definition, RqSN has SN-1 entangled connections in SN, it follows that the WRqSN total entanglement throughput associated with RqSN within the structure of SN (Bell states per C) is as

WRqSN:=q,z:zqQRqSN,RzSN=SN-11SNBRqSN,Snc=SN-11SN2i=1ncBRi. 14

Since there are SN quantum repeaters in SN, the ZSN cumulated entanglement throughput of the quantum repeaters of SN (Bell states per C) is as

ZSN:=q=1SNWRqSN=q=1SNq,z:zqQRqSN,RzSN=q=1SNSN-11SNBRqSN,Snc=SNSN-11SNBRqSN,Snc=SNSN-11SN2i=1ncBRi=SN-11SNi=1ncBRi. 15

Because of the SN strongly-entangled structure has SNSN-1/2 entangled connections, the TSN total entanglement throughput of the entangled connections of SN (Bell states per C) is as

TSN:=SNSN-121SN2i=1ncBRi=SN-121SNi=1ncBRi. 16

Related works

In this section the related works are given.

On the problem of resource allocation and routing in quantum networks, we suggest the works of62,70,71. In70, the authors study the problem of entanglement routing in practical quantum networks with limited quantum processing capabilities and with noisy optical links. The authors study how a practical quantum network can distribute high-rate entanglement simultaneously between multiple pairs of users. In71, the authors study new routing algorithms for a quantum network with noisy quantum devices such that each can store a small number of qubits. In62, the problem of entanglement generation is modeled through a stochastic framework that takes into consideration the key physical-layer mechanisms affecting the end-to-end entanglement rate. The author derives the closed-form expression of the end-to-end entanglement rate for an arbitrary path, and design a routing protocol for quantum networks.

In a quantum Internet scenario, the entanglement purification is a procedure that takes two imperfect systems σ1 and σ2 with initial fidelity F0<1, and outputs a higher-fidelity density ρ such that Fρ>F0. In139, the authors propose novel physical approaches to assess and optimize entanglement purification schemes. The proposed solutions provide an optimization framework of practical entanglement purification.

In140, a satellite-to-ground QKD system has been demonstrated. In141, the authors demonstrated the quantum teleportation of independent single-photon qubits. In142, the authors demonstrated the Bell inequality violation using electron spins. In143, the authors demonstrated modular entanglement of atomic qubits using photons and phonons. For an experimental realization of quantum repeaters based on atomic ensembles and linear optics, see144,145.

Since quantum channels also have a fundamental role in the quantum Internet, we suggest the review paper of137, for some specialized applications of quantum channels. For a review on some recent results of quantum computing technology, we suggest146. For some recent services developed for the quantum Internet, we suggest1217,2729.

Some other related topics are as follows. The works1214,68,69,137,138 are related to the utilization of entanglement for long-distance quantum communications and for a global-scale quantum Internet, and also to the various aspects of quantum networks in a quantum Internet setting137,147155.

A technical roadmap on the experimental development of the quantum Internet has been provided in20, see also156. For some important works on the experimental implementations, we suggest157180.

Method

Resource consumption optimization via entanglement throughput prioritization

The aim of the entanglement throughput prioritization is to find an optimal distribution of the entanglement throughputs of the entangled connections of a given quantum repeater. The prioritization leads to an optimized, nearly uniform distribution of the resource consumptions of the quantum repeaters.

Theorem 1

(Resource consumption of a quantum repeater). The CRi resource consumption of a quantum repeater Ri is adjustable by distributing the weight coefficients associated with the entanglement throughputs of the entangled connections of Ri.

Proof

Let us assume that there are a source node A and a destination node B in the network.

Assuming that the total number of the (logical) incoming entangled connections Llk of quantum repeater Ri is z, the total resource consumption CRi of quantum repeater Ri is defined via the terms of “Resource consumption of a quantum repeater” section, as

CRi:=k=1zαRi,Llk. 17

Then, let χRi be the total number of received entangled states (number of Bell states) in Ri per cycle:

χRi=k=1zBFLlk, 18

which can be rewritten as a multiplication of the BFA number of entangled states outputted by a source node A to path Ps, and a ωPs0,1 weight of an s-th path Ps, taken for all paths that pass through quantum repeater Ri between A and B, as

χRi=PsA,BSPRiωPsA,BBFPsA, 19

where A and B are the source and target nodes associated with path Ps; SPRi is the set of P paths that pass through quantum repeater Ri between A and B, defined as

SPRi:=Psx,yRiPsx,y, 20

with relation

SPRi,A,Bz; 21

where Psx,y is a s-th path between quantum nodes x and y, s=1,,Sxy, where Sxy is the set of paths between x and y and Sxy is the cardinality of Sxy; such that for a given source and target pair A,B of Ps, s=1,,SAB,

s=1SABωPsA,B=1. 22

Using (18), the term in (17) can be rewritten as

CRiχRik=1zRi,Llk+ζRi,Llk+CRi,Llk=PsA,BSPRik=1zωPsA,B|BFPsA|Ri,Llk+ζRi,Llk+CRi,Llk. 23

The result in (23) reveals that a loose upper bound on CRi can be obtained from (17) and (18), and also shows that CRi is adjustable by the weight coefficients ωPs. An aim here is therefore to find the optimal distribution of the weight coefficients.

Assuming that the total number of quantum repeaters is q, the optimization problem can be defined via an objective function fC subject to a minimization as

fC:=minC~Ri,for1iq, 24

where

C~Ri=maxCRi. 25

The problem is therefore to find the optimal distribution for the weights of the paths associated with the entangled connections that minimizes the objective function (24).

Using (22), a constraint ΩRi can be defined for all source and target node pairs x,y that share an entangled connection Llx,y through Ri, as

ΩRi:=s=1SxyωPsx,y=1,forLlx,y, 26

where

0ωPsx,y1. 27

Then, let BFRi,Rj be the entanglement throughput (Bell states per C) between quantum repeaters Ri and Rj connected by the entangled connection LlRi,Rj, with an upper bound BFRi,Rj.

Using (19), a constraint ΓLlRi,Rj can be defined for the P paths that traverse an entangled connection LlRi,Rj between quantum repeaters Ri and Rj (see Fig. 2a), as

ΓLlRi,Rj:=PsA,BSPLlRi,RjRiSPLlRi,RjRjωPsA,B|BFPsA|BFRi,Rj, 28

where SPLlRi,RjRiSPLlRi,RjRj refers to the set of paths that pass through the entangled connection LlRi,Rj between quantum repeaters Ri and Rj, respectively. As follows, in (28), a particular path

PsA,B=LlA,B 29

traverses the entangled connection LlRi,Rj, if only the relation

PsA,BSPLlRi,RjRiSPLlRi,RjRj 30

holds.

Figure 2.

Figure 2

(a) A quantum Internet scenario with a set of incoming entangled connections SPLlRi,RjRiSPLlRi,RjRj that traverse the entangled connection LlRi,Rj between quantum repeaters Ri and Rj. The entangled states in the set SPLlRi,RjRi of Ri and in the set SPLlRi,RjRj of Rj (depicted by gray circles) are to be swapped with the entangled state that forms LlRi,Rj. The entanglement swapping is performed by the entanglement swapping operator US. The other incoming entangled states in the quantum repeaters that do not traverse LlRi,Rj are not elements of SPLlRi,RjRiSPLlRi,RjRj. (b) A deadlock situation in the entanglement swapping procedure in a quantum Internet setting. The aim of quantum node A is to share an entangled connection with the distant quantum repeater Rk. The source node A generates an entangled pair and transmits one half, ρA, to Ri and keeps the other half, RAβρA. In Ri, the set AρA (depicted by a yellow circle) does not contain the target entangled system σB from the target node Rk for the swapping; therefore, Ri generates an entangled pair (depicted by black dots) and shares an entangled connection LlRi,Rj with Rj. Quantum repeater Rj also generates an entangled pair (depicted by blue dots) and shares the entangled connection LlRj,Rk with Rk. Then, the target quantum node Rk generates an entangled connection (depicted by red dots) and sends one half, σB, to Ri to form the entangled connection LlRk,Ri, while it keeps the other half, RkβσB. (c) Quantum repeater Ri receives σB and swaps it with ρA to form the distant entangled connection LlA,Rk. The deadlock in the entanglement swapping is caused by the fact that set AρA in Ri does not contain σB, so Ri does not establish the entangled connection LlRi,Rj with Rj, and Rj does not establish the entangled connection LlRj,Rk with Rk.

Then, let SPN=P1,,Pn be the set of entangled paths, where Pi is an i-th entangled path, with a weighted entanglement throughput ϕPi of the path Pi (Bell states per C), as

ϕPi=ωPiA,BBFPiA, 31

where A is the source node of entangled path Pi, ωPiA,B is the weight associated to PiA,B, while BFPiA is the entanglement throughput (Bell states per C) of the source node A of Pi.

The optimal Dωsx,y distribution of the weights that minimizes fC is determined via Procedure 1.

Procedure 1 assumes a quantum Internet scenario, in which a particular quantum repeater Ri has several different (logical) incoming and (logical) outcoming entangled connections, and the number of paths that traverse a particular quantum repeater is distributed non-uniformly.

graphic file with name 41598_2020_78960_Figa_HTML.jpg

graphic file with name 41598_2020_78960_Figb_HTML.jpg

The schematic model of the resource consumption determination of a quantum repeater is depicted in Fig. 1.

Figure 1.

Figure 1

The schematic model of the resource consumption evaluation of a quantum repeater Ri in a quantum Internet scenario. The quantum repeater has z incoming entangled connections, Llk, k=1,,z, from among SPRi=2 paths, PsA,B, s=1,2, that pass through quantum repeater Ri between A and B. The paths P1A,B and P2A,B are associated with the weighted entanglement throughput values ωP1A,BBFPsA and ωP2A,BBFPsA, where ωPsA,B0,1 are the path weights and BFPsA is the entanglement throughput (Bell states per C) of the source A of the path Ps. (The entangled states associated with the entangled connections in the quantum repeater are depicted by green, brown, and black dots.)

Entanglement swapping prioritization

Because the distribution of the weights ωPsA,B is determined via Procedure 1, the task in a given quantum repeater is then to determine the set of entangled states associated with the weighted entangled connections for the entanglement swapping procedure.

Lemma 1

(Entanglement swapping probability and the weights of entangled connections). The probability PrUSρA,σB,i of entanglement swapping US between a source ρA and a target density matrix σB,i in a quantum repeater depends on the weights associated with the swapped entangled connections.

Proof

Let

PrUSρA,σB,i=PrσB,i,RsβρA,RdβσB,i

be the probability that density σB,i is selected from AρA to the entanglement swapping with ρA by swapping operator US.

Since set AρA contains r possible entangled states for the entanglement swapping,

i=1rPrUSρA,σB,i=i=1rPrσB,i,RsβρA,RdβσB,i=1, 43

where probability PrσB,i,RsβρA,RdβσB,i is evaluated as

PrσB,i,RsβρA,RdβσB,i=Psx,yκPωsLlRsβρA,RdβσB,iBFLlRsβρA,RdβσB,i, 44

where

κP=SPσB,i,RsβρA,RdβσB,i, 45

and BFLlRsβρA,RdβσB,i is the entanglement throughput (Bell states per C) of the entangled connection LlRsβρA,RdβσB,i, while ωsLlRsβρA,RdβσB,i is the weight associated with an s-th path over LlRsβρA,RdβσB,i (see also Fig. 2a).

Assuming that for each σB,i there exist a source set QσB,i of g input entangled states,

QσB,i=ρA,1,,ρA,g, 46

the probability PrσB,i,QσB,i,RdβσB,i can be yielded as

PrσB,i,QσB,i,RdβσB=ρA,kQσB,iPsx,yκP,kωsLlRsβρA,k,RdβσB,iBFLlRsβρA,k,RdβσB,i, 47

where

κP,k=SPσB,i,RsβρA,k,RdβσB,i, 48

and

i=1rPrσB,i,QσB,i,RdβσB,i=1. 49

Entanglement swapping deadlock

The entangled state selection procedure of the entanglement swapping in a quantum repeater Ri can lead to a deadlock in the establishment of an entangled connection LlRi,Rd between Ri, and a distant quantum repeater Rd.

An entanglement swapping situation in a quantum Internet scenario is depicted in Fig. 2b, c, respectively.

The problem of deadlock-free entanglement swapping is discussed in Section A.1 of the Supplemental Information.

Strongly-entangled structure for resource balancing in the quantum internet

A quantum network structure called the strongly-entangled quantum network is defined. The aim of this network is optimal resource balancing within the quantum Internet to take care of problematic situations. The problematic situation considered here is the serving of an arbitrary number of low-priority quantum nodes. A low-priority quantum node cannot be served by an actual quantum node in the network due to resource issues or an arbitrary network issue. Instead, the set of low-priority nodes are served through the strongly-entangled quantum network, which comprises an arbitrary number of quantum repeaters such that all quantum repeaters are entangled with each other. The strongly-entangled structure represents a resource that can manage issues in the network. In the serving procedure of the low-priority nodes, the quantum repeaters are selected uniformly at random to handle the density matrix of a low-priority node. The randomized behavior leads to a random routing between the low-priority nodes and the quantum repeaters, as well as to optimal resource balancing within the network. It is also assumed that the strongly-entangled structure has connections with many subnetworks.

Resource allocation

In this section, the network situation is modeled via the definitions of “Strongly-entangled structure” section. A density matrix of Ri is associated with an RISN ingress quantum repeater of SN selected uniformly at random, thus a random routing is performed for the incoming query from the low-priority node Ri to SN. Then, an arbitrary routing is preformed between the RESN egress quantum repeater of SN and the DRi destination node of Ri.

The quantum nodes and the entangled connections of the SN structure are characterized as follows. Let RqSN be an q-th, q=1,,SN, quantum repeater in SN, and let BRiSN,Ri be the entanglement throughput request (Bell states per C) of the low-priority node Ri. The structure of a SN strongly-entangled quantum network is depicted in Fig. 4.

Figure 4.

Figure 4

The strongly-entangled structure SN as formed by SN quantum repeaters and ESN entangled connections with heterogeneous entanglement levels, where SN=5, ESN=SN·SN-12 , and FSN=1-1SN. (a) The low-priority node Ri is associated with the entanglement throughput request BRi. The SN quantum repeaters of SN establish SN entangled connections with Ri (depicted by the outgoing dashed black lines), with each connection having entanglement throughput BRqSN,Ri=1SNBRi, where q=1SNBRqSN,Ri=BRi. A given quantum repeater RqSN of SN establishes SN-1 entangled connections within SN, each with entanglement throughput QRqSN,RzSN=1SNBRqSN,Ri, where RzSN is a neighbor of RqSN. (b) Each of the SN-1 quantum repeaters of SN applies entanglement swapping US to establish the entangled connection between Ri and the egress quantum repeater RESN of SN. Then, an arbitrary routing is applied to establish the entangled connection between Ri and the destination node DRi of Ri. The request from Ri to the strongly-entangled structure SN is served via nP=SN parallel entangled paths PRqSN,Ri between the quantum repeaters of SN and Ri. The dashed entangled connections are rebuilt within SN after the entanglement swapping operations.

Theorem 2

(Handling resource issues via a strongly-entangled structure). Let Ri be a low-priority quantum node with a non-servable resource request. The problem of resource allocation can be handled by a strongly-entangled quantum network structure SN and a random routing R between the quantum repeaters of SN and Ri.

Proof

The structure of SN allows to RqSN to split the BRiSN,Ri entanglement throughput request to SN smaller, BRiSN,Ri/SN requests. As follows, within the structure of SN, the entangled connection between quantum repeaters RiSN and RqSN, is associated with the following entanglement throughput (Bell states per C):

QRiSN,RqSN=BRiSN,RiSN,forq=1,,SN-1,qi. 50

As follows, using SN, the entanglement throughputs of all of the SN-1 entangled connections of RiSN are associated with the 1/SN-th of the incoming request of RiSN. Therefore, the incoming of RiSN request is divided into SN fractions and distributed to the SN-1 neighbors of RiSN in the strongly-entangled structure SN.

As the quantum repeaters of SN shared the entangled systems with each other, a random routing is utilized from all quantum repeaters of SN to the low-priority node Ri. The request from Ri to the strongly-entangled structure SN is served via

nP=SN 51

parallel entangled paths PRqSN,Ri between the quantum repeaters of SN and Ri.

Therefore, the source of a PRqSN,Ri entangled path is the q-th quantum repeater RqSN from SN, q=1,,SN, while the target is Ri. The SN parallel entangled paths define the set RS of random quantum repeaters used in the routing procedure as

RS=RR1SN,RiRRSNSN,Ri, 52

where RRiSN,Ri identifies a set of random nodes used in the random routing R from RiSN to Ri.

As the entangled paths are established, an USRqSN entanglement swapping operation is applied in all of the RqSN quantum repeaters of SN. The aim of these operations is to swap the entangled connections to the egress quantum repeater RESN of SN.

The result is SN entangled connections between Ri and RESN, i.e., the set of SN entangled paths

PRi,B=P1Ri,RESNPSNRi,RESN 53

such that the BFPRi,RESN entanglement throughput of entangled path PjRi,RESN is as

BFPjRi,RESN=BRqSN,Ri=1SNBRi, 54

where BRi is the total entanglement throughput request of Ri (Bell states per C), since the entangled path of RqSN and Ri is swapped to the path between RESN and Ri via a swapping USRqSN in RqSN.

Therefore the sum of the entanglement throughput of the SN entangled paths (Bell states per C) is

j=1SNBFPjRi,RESN=q=1SNBRqSN,Ri=BRi, 55

thus it equals to the entanglement throughput request received from the low-priority node Ri.

Assuming that there are nc low-priority quantum nodes in N all with different entanglement throughput requests, the SN strongly-entangled structure has to serve all of these nc low-priority quantum nodes simultaneously. In this case, the steps detailed above are established in parallel for all of the nc low-priority nodes, thus the nΣP total number of parallel entangled connections established via the SN structure is

nΣP=ncSN. 56

As the LlRi,RESN entangled connection is built up via the entanglement swapping in RISN, an arbitrary routing from RESN to DRi can be used to construct the entangled connection LlRESN,DRi. Then, an entanglement swapping in RESN yields the long-distance LlRi,DRi entangled connection.

The construction method of a strongly-entangled structure is given Procedure 2.

Figure 3 depicts a quantum Internet scenario with the requirement of resource balancing in the quantum repeaters of the entanglement distribution process.

Figure 3.

Figure 3

Entanglement distribution with resource balancing in the quantum Internet. (a) Low-priority quantum repeaters. Users U3 and U6 would like to share an entangled connection with B through Rl. Quantum repeater Rl has only a single density matrix from B available for the entanglement swapping via entangled connection LlRk,B, and as a corollary, Rl can serve only U3 or U6. Users U1, U2, and U5 are served directly, since these users have no common resource requirements. The quantum repeater node Rl serves U6, thus establishing the entangled connection LlRk,Rl between Rk and Rl. (b) Resource balancing via random routing. User U6 establishes the distant entangled connection LlRk,B with B through Rl (depicted by the green line). For a seamless transition of resource saving, a random quantum repeater is selected for user U3 from the set RS of random quantum repeaters (RS is realized by the strongly-entangled structure SN) to establish the entangled connection LlRi,R (depicted by the red line), where RRS is a quantum repeater from RS.

graphic file with name 41598_2020_78960_Figc_HTML.jpg

Resource balancing

Theorem 3

(Capability of the strongly-entangled structure). The strongly-entangled structure SN provides a structure to serve all the nc low-priority quantum nodes simultaneously.

Proof

Using the metrics defined in “Capability of a strongly-entangled structure” section, first we derive some relevant attributes of SN.

From (14), the FRqSN fanout (ratio of the WRqSN total entanglement throughput (14) within SN and the BRqSN,Snc incoming request from the low-priority quantum repeaters, (12) of a quantum repeater RqSN at nc low-priority quantum repeaters is defined as

FRqSN:=WRqSNBRqSN,Snc=SN-11SN2i=1ncBRi1SNi=1ncBRi=1SNSN-1=1-1SN, 57

and the FSN fanout of SN as the maximum fanout among the quantum repeaters of SN as

FSN:=maxqFRqSN=1-1SN, 58

such that

maxqWRqSN1SN2i=1ncBRiZSNBSN, 59

by theory135,136, where ZSN is as given in (15), while BSN is the total requests from the nc quantum repeaters to SN (Bell states per C) as

BSN=q=1SNBRqSN,Snc=i=1ncBRi. 60

Thus, (59) can be rewritten as

FSN=SN-11SNi=1ncBRiSN1SNi=1ncBRi=1-1SN. 61

As a corollary, FSN1 for any SN1, while in a classical full-mesh structure M, the fanout is lower bounded by 1, i.e. FM1. As follows, the FSN1 property is strictly resulted from the attributes of the quantum structure (such as entanglement swapping), and it cannot be achieved within any classical full-mesh structure-based uniform load-balancing135,136.

Note, that in (61) it is assumed that within the structure of SN, all the RqSN are associated with the same BRqSN,Snc values (see (12)), and a corollary, the throughputs of the entangled connections within SN are set equally to QRqSN,RzSN (see (13)), since each quantum repeater receive the same amount of incoming request. Let us to derive FSN for the case if the BRqSN,Snc values of SN are not equally set, while the condition

q=1SNBRqSN,Snc=i=1ncBRi. 62

holds for the BRqSN,Snc values in ingress quantum repeaters.

In this case, (15) is as

ZSN=q=1SNSN-11SNBRqSN,Snc, 63

thus FSN is yielded as

FSN=q=1SNSN-11SNBRqSN,SncBSN=SN-11SNi=1ncBRii=1ncBRi=SN-11SN, 64

thus (64) picks up its minimum (61) if the incoming density matrices of SN are not uniformly distributed.

On the other hand, if

q=1SNBRqSN,Snc<i=1ncBRi, 65

such that

q=1SNBRqSN,Snc+x=i=1ncBRi 66

while the internal entangled connections of SN are set with relation qQRqSN,RzSN=i=1ncBRi, then

FSN=SN-11SNi=1ncBRiBSN-x>SN-11SN. 67

On the relation of the incoming request and the internal entanglement throughputs of the entangled connections some derivations are as follows.

Let BRqSN,Snc be the entanglement throughput request from the nc low-priority nodes to RqSN (Bell states per C), and let RESN be the egress node of the requests with entangled connection LlRqSN,RESN.

If the entanglement throughput QRqSN,RESN within SN is set as

QRqSN,RESNBRqSN,Snc, 68

then a request from RqSN to RESN can be served, while if

QRqSN,RESN<BRqSN,Snc, 69

the request BRqSN,Snc is served through different LlRiSN,RjSN entangled connections in SN, such that

i<jQRiSN,RjSNQRqSN,RESN, 70

and

i<jQRiSN,RjSNBRqSN,Snc. 71

Assuming that (68) holds for all quantum repeaters of SN, then

qEQRqSN,RESNqEBRqSN,Snc, 72

while if q=E, then the RqSN node is also the egress node, thus the aim is to achieve an arbitrary routing from RqSN to the distant node associated with the incoming request that is not part of the structure SN.

The proof is concluded here.

The schematic model of the strongly-entangled structure SN is illustrated in Fig. 4.

Lemma 2

(Resource-balancing efficiency of the strongly-entangled structure SN for nc low-priority nodes).In terms of fanout minimization and total traffic minimization, the strongly-entangled quantum network structure SN is two times more efficient than a classical full-mesh network structure M.

Proof

First, we compare the fanout coefficients of the classical full-mesh structure M and the strongly-entangled quantum network SN. Then, we compare the total amount of traffic within the structures of M and SN.

For simplicity, let us assume that M=SN and that the nodes of the structures are associated with the same incoming traffic (measured in the number of packets for M, and the number of density matrices for SN):

TxqM,xi=BRqSN,Ri, 73

where T· is the traffic of M, B· is the traffic of SN, xi is a source node, xqM is the q-th node of M, Ri is a source quantum repeater, and RqSN is the q-th quantum repeater of SN.

It can be verified135,136, that for structure M, the fanout coefficient FM is

FM=2M-11M=21-1M, 74

where FSN is as in (58). The fanout of the entangled structure is half of the fanout of M; thus, μFSN,FM, the ratio of FSN to FM, trivially follows: μFSN,FM=FSNFM=12.

Therefore, in terms of fanout minimization, the strongly-entangled structure is two times more efficient than a classical full-mesh structure.

In terms of the total traffic required within the structures, the results are as follows.

It can be proven that in the classical full-mesh structure M, two phases of communications are required to establish a communication between a low-priority node xi and an egress node xEM of M. In the first phase, the ingress node xIM of M transmits the incoming packet to a random intermediate node of M. In the second phase, the packet is transmitted from xzM to the exit node xEM of M. Accordingly, an incoming packet traverses M twice135,136.

On the other hand, in the strongly-entangled structure SN, only the first phase is required for seamless routing. The second step can be replaced via the entanglement swapping operator; thus, the incoming densities can be entangled with the target node without a second phase transmission.

In SN, all quantum repeaters share an entangled connection with the low-priority node; thus, in a quantum repeater RqSN of SN only an entanglement swapping USRqSN is required to establish an entangled connection between the low-priority node Ri and the egress quantum repeater RESN of SN. Therefore, as the entangled path is established from RESN to DRi, a swapping USRESN in RESN connects DRi with the low-priority node Ri. Accordingly, in the strongly-entangled structure SN, it is enough to apply only one phase to serve Ri via RESN, whereas M requires two phases.

The corollaries for the amount of traffic within the structures are as follows. In M, each node uniformly load-balances its incoming traffic to the other nodes of the structure, regardless of the destination, and then all packets are delivered to the final destination via an egress node by an arbitrary routing135,136. The two phases within M require a total traffic

Txi,M=2M-1TxIM,xiM. 75

In SN, since only the first phase is required, it reduces the total traffic to

BRi,SN=SN-1BRqSN,RiSN; 76

thus from (75) and (76), the ratio of the total transmissions within M and SN is

μRi,xi=BRi,SNTxi,M=12. 77

Then, let us further assume that there are nc low-priority nodes with a node set Snc and that each node xiM of M is an ingress node receiving incoming traffic TxI,iM,Sinc from Sinc, where Sinc is the i-th subset of Snc.

In this case, the total traffic in M is TSnc,M=i=1M2M-1TxI,iM,SincM,

whereas for the structure SN,

BSnc,SN=q=1SNSN-1BRqSN,SincSN; 78

thus, the ratio of the total traffic in the structures is also 1/2, since

μSN,M=BSnc,SNTSnc,M=12. 79

Assuming that the incoming traffic is the same for all ingress nodes in the structures of M and SN, the result in (5.2) simplifies as

TSnc,M=MM-122TxIM,SincM=M-1TxIM,Sinc, 80

while (78) can be rewritten as

BSnc,SN=SNSN-12BRqSN,SincSN=SN-12BRqSN,Sinc; 81

thus the ratio of (79) also follows.

As a corollary, using the total entanglement throughput TSN (16) of the entangled connections of SN (Bell states per C) and the total traffic TM of M,

TM=M-1i=1ncTxi, 82

the ratio

μTSN,TM=TSNTM=12 83

follows.

Therefore, with respect to the amount of total traffic, the proposed strongly-entangled network structure SN is two times more efficient than a classical full-mesh network structure M.

The proof is concluded here.

Random routing

Theorem 4

(Random routing efficiency via the strongly-entangled structure). The structure SN enables an efficient random routing for all the nc low-priority quantum repeaters Ri, i=1,,nc, via the total number of entanglement swapping operations USSN,Ri in SN for the serving of Ri, with PrUSSN,Ri2c12SN, for any c1.

Proof

Our aim here is to show that the probability that more than entanglement swapping operation is required in a particular uniform randomly selected RISN ingress quantum repeater of SN to construct the entangled path between the source Ri and egress quantum repeater RESN of SN is low.

Let SN be the number of RqSN quantum repeaters, q=1,,SN in the strongly-entangled structure SN, and let ESN be the number of entangled connections within SN, ESN=SN·SN-12.

Then, let Ri a source quantum node from the set Snc of the nc low-priority quantum repeaters, Snc=nc. Then, a given RISN ingress quantum repeater is selected for Ri with probability

PrRiRISN=1SN, 84

to formulate the random entangled path Pi from Ri to RISN,

Pi=PRISN,Ri. 85

Then, let assume that a random path Pi requires the egress quantum repeater RESN, that formulates entangled path between Ri and RISN

Pi=PRESN,Ri. 86

Let f· be an indicator function, defined as

fPi,Pj:=1,ifRISNPi=RISNPjRESNPi=RESNPj0,otherwise, 87

where RISNPxSN and RESNPxSN are the ingress and egress quantum repeaters of path Px. Thus, the indicator function indicates an event if the RISNPi ingress node of Pi coincidences with the RISNPj ingress node of Pj and the RESNPi egress node of Pi coincidences with the RESNPj egress node of Pj. Thus, a fPi,Pj=1 situation therefore indicates a collision between the paths Pi and Pj (A collision situation is illustrated in Fig. 5.).

Figure 5.

Figure 5

A collision of entangled paths in the strongly-entangled structure. The ingress and egress quantum repeaters associated with the paths within the strongly-entangled structure coincide. Entangled path Pi is depicted by a red dashed line, and entangled path Pj is depicted by a blue dashed line.

Since for Pi and Pj, the RISN ingress quantum repeaters are selected independently and uniformly random within Snc, it follows that for the entangled paths Pi,Pj,Pk, the fPi,Pj and fPi,Pk indicator functions are independent random variables for ijki.

As follows, indicator functions fPi,Pj and fPi,Pk can be rewritten as Bernoulli random variables

Xj=fPi,Pj 88

and

Xk=fPi,Pk, 89

such that

PrXj=1=EXjPSNESN, 90

wherePSN is the path length within SN, such that

PSN=1 91

due to the structural attributes of SN. (Thus, (91) holds because only one entangled connection within SN is required for the swapping from the ingress node to an egress node.).

As follows, (90) can be rewritten as

PrXj=1=EXj1SN·SN-12=2SN·SN-1 92

Taking (92) for all the SN nodes, yields a tail distribution for the sum of SN Bernoulli variables, as

PrXΣx, 93

where XΣ is the sum of SN Bernoulli random variables,

XΣ=j=1SNXj, 94

for any positive x, with a relation by Markov inequality

PrXΣxSNEXjx=SN2SN·SN-1x=2xSN-11x. 95

Then, since (95) is not sufficiently small if

x=2c 96

for any constant c, (95) can be reformulated as

PrXΣxPrenXΣenxEenXΣenx, 97

for any positive n.

Thus, from the Chernoff-bound181, the relation

PrXΣxminn>0EenXΣenx, 98

follows.

Since XΣ is the sum of SN Bernoulli random variables, EenXΣ can be evaluated as

EenXΣ=Eenj=1SNXj=j=1SNEenXj=EenXjSN=pen+1-pSN=1+pen-1SN, 99

that can be rewritten as

EenXΣeen-1SNEXj, 100

since 1+aea, by theory.

Therefore, PrXΣx can be rewritten as

PrXΣx=minn>0een-1SNEXjenx, 101

thus at

n=ln1+ξ 102

the following relation is yielded

PrXΣ1+ξSNEXjeξ1+ξ1+ξSNEXj. 103

It can be verified, that if ξ is sufficiently large, then (103) can be rewritten as

PrXΣcSNEXj12SNEXj, 104

thus the probability that for a given resource node Ri with path Pi more than one USSN,Ri entanglement swapping operation is required within SN to construct the entangled path between Ri and RESNPi, is yielded as

PrUSSN,Ri2c12SN, 105

where c1 is a positive integer, while USSN,Ri is the number of entanglement swapping operations within SN associated with Ri.

The proof is therefore concluded here.

A path collision between entangled paths Pi and Pj in the strongly-entangled structure SN is illustrated in Fig. 5. Both entangled paths are associated with the same ingress node RISNPi=RISNPj and egress node RESNPi=RESNPj. Assuming that RISN and RESN share only one entangled connection within SN, only the serving of one path from RISN to RESN is allowed. Path Pi is served via the entangled connection between RISN and RESN, while the serving of Pj is decomposed as RISNRzSNRESN, where RzSNRESN.

Fault tolerance

Theorem 5

(Fault tolerance of the strongly-entangled structure). The strongly-entangled structure provides a seamless service at 1kSN-2 arbitrary entangled connection failures by increasing the entanglement throughputs of the remaining ESN-k entangled connections of SN.

Proof

Let QRqSN,RzSN=1SNBRqSN,Ri be the entanglement throughputs (see (50)) of the entangled connections within SN at no failures.

At k entangled connection failures, let Δk be the increment of the entanglement throughputs (Bell states per C) of the remaining ESN-k entangled connections of SN, and let

QkRqSN,RzSN=QRqSN,RzSN+Δk 106

be the updated entanglement throughputs of the entangled connections of SN (Bell states per C).

Let us define entangled connection failure events E1, E2 and E3 in the following manner:

E:=E1,ifk=1E2,ifk=2k=SN-2SN6E3,otherwise. 107

Then, using the formalisms of136, after some calculations Δk can be evaluated as

Δk=121SN-2BRqSN,Snc+1SNBRqSN,Snc-1SNBRqSN,Snc,ifE=E1121SN-k-1BRqSN,Snc+1SN-1BRqSN,Snc-1SNBRqSN,Snc,ifE=E21SN-kBRqSN,Snc-1SNBRqSN,Snc,ifE=E3. 108

As follows, at an initial QRqSN,RzSN=1SNBRqSN,Ri, the updated QkRqSN,RzSN at the failure of k entangled connections in SN is on the order of

QkRqSN,RzSN1SN-kBRqSN,Snc. 109

As h quantum repeater RqSN fails within SN, then the structure of SN becomes a strongly-entangled network formulated by SN-h quantum repeaters, therefore Δh is yielded as

Δh=1SN-kBRqSN,Snc-1SNBRqSN,Snc. 110

If both k entangled connections and h quantum repeater RqSN fails in the structure, then the problem is analogous to k entangled connection failures within a strongly-entangled structure formulated by SN-h quantum repeaters135,136. Therefore, Δk,h can be evaluated via (108) and (110) in the following manner:

Δk,h=121SN-h-2BRqSN,Snc+1SN-hBRqSN,Snc-1SNBRqSN,Snc,ifE=E1121SN-h-k-1BRqSN,Snc+1SN-h-1BRqSN,Snc-1SNBRqSN,Snc,ifE=E21SN-h-kBRqSN,Snc-1SNBRqSN,Snc,ifE=E3. 111

Performance evaluation

Here, we analyze the performance of the strongly-entangled structure SN and compare it with a classical full-mesh structure M. Using the results of “Method” and “Strongly-entangled structure for resource balancing in the quantum internet” sections, a numerical evidence is given to characterize the amount of transmitted traffic within the structures as a function of the number of nodes, to characterize the fanout coefficients of the structures as a function of the number of nodes, and to compare the traffic increments of the connections at connection failures. For the comparison between classical resource balancing and quantum resource balancing, the results of “Method” and “Strongly-entangled structure for resource balancing in the quantum internet” sections are compared with the results of135,136.

In Fig. 6a the amounts of traffic are compared within a strongly-entangled structure SN and a classical full-mesh structure M. In Fig. 6b, the fanout coefficients of the structures are compared. In Fig. 6c compares the fault tolerant capabilities of the structures.

Figure 6.

Figure 6

(a) Comparison of the amounts of traffic TSnc,M and BSnc,SN (Bell states per C) within the structures of M and SN, with TxIM,Sinc=BRqSN,Sinc=100 and M=SN=1,,100. (b) Comparison of the fanout coefficients FM and FSN of the structures of M and SN, with M=SN=1,,100. (c) The entanglement throughput increment Δk (Bell states per C) of the ESN-k entangled connections at the failure of k entangled connections within SN, with k=1,,100, SN=100, BRqSN,Ri=100, and QRqSN,RzSN=1SNBRqSN,Ri=1, QkRqSN,RzSN1SN-kBRqSN,Snc. For the comparison between classical resource balancing and quantum resource balancing, the proposed results are compared with the results of135,136.

The strongly-entangled quantum network is two times more effective than a classical full-mesh structure: The required amount of traffic is half that of the classical structure135,136, the fanout coefficient of the strongly-entangled structure is half that of the classical structure, and the required entanglement throughput of the entangled connection is half that of the classical structure. As future work, our aim is to provide a detailed performance comparison with other related approaches on resource allocation and routing in quantum networks62,70,71.

Conclusions

Here, we defined methods and procedures for optimizing the resource allocation mechanisms of the quantum Internet. We proposed a model for resource consumption optimization of quantum repeaters, proposed a method for optimizing the entanglement swapping procedure, and studied the conditions of deadlock-free entanglement swapping. We defined a strongly-entangled network structure for optimal resource balancing in the quantum Internet. We proved the resource-balancing efficiency of the strongly-entangled structure and its fault tolerance.

Supplementary information

Supplementary material 1. (148.6KB, pdf)

Acknowledgements

Open access funding provided by Budapest University of Technology and Economics (BME). The research reported in this paper has been supported by the Hungarian Academy of Sciences (MTA Premium Postdoctoral Research Program 2019), by the National Research, Development and Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program), by the National Research Development and Innovation Office of Hungary (Project No. 2017-1.2.1-NKP-2017-00001), by the Hungarian Scientific Research Fund - OTKA K-112125, in part by the BME Artificial Intelligence FIKP grant of EMMI (Budapest University of Technology, BME FIKP-MI/SC), and by the Ministry of Innovation and Technology and the National Research, Development and Innovation Office within the Quantum Information National Laboratory of Hungary.

Author contributions

L.G.Y. designed the protocol and wrote the manuscript. L.G.Y. and S.I. and analyzed the results.

Data availability

This work does not have any experimental data.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

is available for this paper at 10.1038/s41598-020-78960-5.

References

  • 1.Gyongyosi, L. & Imre, S. Resource Prioritization and Resource Balancing for the Quantum Internet, FIO19 Proceedings. Washington DC. United States. 10.1364/FIO.2019.JTu4A.47 (2019).
  • 2.Van Meter, R. Quantum Networking. ISBN 1118648927, 9781118648926, Wiley (2014).
  • 3.Lloyd S, et al. Infrastructure for the quantum internet. ACM SIGCOMM Compu. Commun. Rev. 2004;34:9–20. doi: 10.1145/1039111.1039118. [DOI] [Google Scholar]
  • 4.Kimble HJ. The quantum internet. Nature. 2008;453:1023–1030. doi: 10.1038/nature07127. [DOI] [PubMed] [Google Scholar]
  • 5.Gyongyosi, L. Services for the Quantum Internet. D.Sc. Dissertation, Hungarian Academy of Sciences (MTA) (2020).
  • 6.Gyongyosi L. Dynamics of entangled networks of the quantum internet. Sci. Rep. 2020 doi: 10.1038/s41598-020-68498-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Gyongyosi L, Imre S. Routing space exploration for scalable routing in the quantum internet. Sci. Rep. 2020 doi: 10.1038/s41598-020-68354-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Gyongyosi L, Imre S. Entanglement concentration service for the quantum internet. Quantum Inf. Process. 2020 doi: 10.1007/s11128-020-02716-3. [DOI] [Google Scholar]
  • 9.Gyongyosi L, Imre S. Optimizing high-efficiency quantum memory with quantum machine learning for near-term quantum devices. Sci. Rep. 2020 doi: 10.1038/s41598-019-56689-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Gyongyosi L, Imre S. Theory of noise-scaled stability bounds and entanglement rate maximization in the quantum internet. Sci. Rep. 2020 doi: 10.1038/s41598-020-58200-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Gyongyosi L, Imre S. Entanglement accessibility measures for the quantum internet. Quantum Inf. Process. 2020 doi: 10.1007/s11128-020-2605-y. [DOI] [Google Scholar]
  • 12.Gyongyosi L, Imre S. Decentralized base-graph routing for the quantum internet. Phys. Rev. A Am. Phys. Soc. 2018 doi: 10.1103/PhysRevA.98.022310. [DOI] [Google Scholar]
  • 13.Gyongyosi, L. & Imre, S. Dynamic topology resilience for quantum networks. In Proceedings of SPIE 10547, Advances in Photonics of Quantum Computing, Memory, and Communication XI, 105470Z; 10.1117/12.2288707 (2018).
  • 14.Gyongyosi L, Imre S. Topology adaption for the quantum internet. Quantum Inf. Process. 2018 doi: 10.1007/s11128-018-2064-x. [DOI] [Google Scholar]
  • 15.Gyongyosi L, Imre S. Entanglement access control for the quantum internet. Quantum Inf. Process. 2019 doi: 10.1007/s11128-019-2226-5. [DOI] [Google Scholar]
  • 16.Gyongyosi L, Imre S. Opportunistic entanglement distribution for the quantum internet. Sci. Rep. Nat. 2019 doi: 10.1038/s41598-019-38495-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Gyongyosi L, Imre S. Adaptive routing for quantum memory failures in the quantum internet. Quantum Inf. Process. 2018 doi: 10.1007/s11128-018-2153-x. [DOI] [Google Scholar]
  • 18.Pirandola S, Braunstein SL. Unite to build a quantum internet. Nature. 2016;532:169–171. doi: 10.1038/532169a. [DOI] [PubMed] [Google Scholar]
  • 19.Pirandola S. End-to-end capacities of a quantum communication network. Commun. Phys. 2019;2:51. doi: 10.1038/s42005-019-0147-3. [DOI] [Google Scholar]
  • 20.Wehner S, Elkouss D, Hanson R. Quantum internet: a vision for the road ahead. Science. 2018;362:6412. doi: 10.1126/science.aam9288. [DOI] [PubMed] [Google Scholar]
  • 21.Pirandola S, Laurenza R, Ottaviani C, Banchi L. Fundamental limits of repeaterless quantum communications. Nat. Commun. 2017;8:15043. doi: 10.1038/ncomms15043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Pirandola S, et al. Theory of channel simulation and bounds for private communication. Quantum Sci. Technol. 2018;3:035009. doi: 10.1088/2058-9565/aac394. [DOI] [Google Scholar]
  • 23.Pirandola S. Bounds for multi-end communication over quantum networks. Quantum Sci. Technol. 2019;4:045006. doi: 10.1088/2058-9565/ab3f66. [DOI] [Google Scholar]
  • 24.Pirandola, S. Capacities of repeater-assisted quantum communications. arXiv:1601.00966 (2016).
  • 25.Pirandola S, et al. Advances in quantum cryptography. Adv. Opt. Photon. 2020 doi: 10.1364/AOP.361502. [DOI] [Google Scholar]
  • 26.Laurenza R, Pirandola S. General bounds for sender-receiver capacities in multipoint quantum communications. Phys. Rev. A. 2017;96:032318. doi: 10.1103/PhysRevA.96.032318. [DOI] [Google Scholar]
  • 27.Gyongyosi L, Imre S. Multilayer optimization for the quantum internet. Sci Rep. Nat. 2018 doi: 10.1038/s41598-018-30957-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Gyongyosi L, Imre S. Entanglement availability differentiation service for the quantum internet. Sci. Rep. Nat. 2018 doi: 10.1038/s41598-018-28801-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Gyongyosi L, Imre S. Entanglement-gradient routing for quantum networks. Sci. Rep. Nat. 2017 doi: 10.1038/s41598-017-14394-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Gyongyosi L, Bacsardi L, Imre S. A survey on quantum key distribution. Infocom. J X. 2019;I(2):14–21. [Google Scholar]
  • 31.Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2:79. doi: 10.22331/q-2018-08-06-79. [DOI] [Google Scholar]
  • 32.Arute F, et al. Quantum supremacy using a programmable superconducting processor. Nature. 2019 doi: 10.1038/s41586-019-1666-5. [DOI] [PubMed] [Google Scholar]
  • 33.Aaronson, S. & Chen, L. Complexity-theoretic foundations of quantum supremacy experiments. Proceedings of the 32nd Computational Complexity Conference, CCC ’17, 22:1–22:67 (2017).
  • 34.Harrow AW, Montanaro A. Quantum computational supremacy. Nature. 2017;549:203–209. doi: 10.1038/nature23458. [DOI] [PubMed] [Google Scholar]
  • 35.Foxen B, et al. Demonstrating a continuous set of two-qubit gates for near-term quantum algorithms. Phys. Rev. Lett. 2020;125:120504. doi: 10.1103/PhysRevLett.125.120504. [DOI] [PubMed] [Google Scholar]
  • 36.Harrigan, M. et al.Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor. arXiv:2004.04197v1 (2020).
  • 37.Rubin N, et al. Hartree–Fock on a superconducting qubit quantum computer. Science. 2020;369(6507):1084–1089. doi: 10.1126/science.abb9811. [DOI] [PubMed] [Google Scholar]
  • 38.Arute, F. et al.Observation of separated dynamics of charge and spin in the Fermi–Hubbard model. arXiv:2010.07965 (2020).
  • 39.Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm. arXiv:1411.4028 (2014).
  • 40.Farhi, E., Goldstone, J., Gutmann, S. & Neven, H. Quantum Algorithms for Fixed Qubit Architectures. arXiv:1703.06199v1 (2017).
  • 41.Alexeev, Y. et al.Quantum Computer Systems for Scientific Discovery. arXiv:1912.07577 (2019).
  • 42.Loncar, M. et al.Development of Quantum InterConnects for Next-Generation Information Technologies. arXiv:1912.06642 (2019).
  • 43.Ajagekar A, Humble T, You F. Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization Problems. Comput. Chem. Eng. 2020;132:106630. doi: 10.1016/j.compchemeng.2019.106630. [DOI] [Google Scholar]
  • 44.Ajagekar A, You F. Quantum computing for energy systems optimization: challenges and opportunities. Energy. 2019;179:76–89. doi: 10.1016/j.energy.2019.04.186. [DOI] [Google Scholar]
  • 45.IBM. A new way of thinking: The IBM quantum experience. URL: http://www.research.ibm.com/quantum. (2017).
  • 46.Farhi, E., Gamarnik, D. & Gutmann, S. The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case.arXiv:2004.09002v1 (2020).
  • 47.Farhi, E., Gamarnik, D. & Gutmann, S. The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: Worst Case Examples. arXiv:arXiv:2005.08747 (2020).
  • 48.Lloyd, S. Quantum Approximate Optimization is Computationally Universal. arXiv:1812.11075 (2018).
  • 49.Sax, I. et al. Approximate approximation on a quantum annealer. Proceedings of the 17th ACM International Conference on Computing Frontiers (CF 2020) (2020).
  • 50.Brown KA, Roser T. Towards storage rings as quantum computers. Phys. Rev. Accel. Beams. 2020;23:054701. doi: 10.1103/PhysRevAccelBeams.23.054701. [DOI] [Google Scholar]
  • 51.Gyongyosi L. Quantum state optimization and computational pathway evaluation for gate-model quantum computers. Sci. Rep. 2020 doi: 10.1038/s41598-020-61316-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Gyongyosi L, Imre S. Dense quantum measurement theory. Sci. Rep. 2019 doi: 10.1038/s41598-019-43250-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Gyongyosi L. Objective function estimation for solving optimization problems in gate-model quantum computers. Sci. Rep. 2020 doi: 10.1038/s41598-020-71007-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Gyongyosi L. Decoherence dynamics estimation for superconducting gate-model quantum computers. Quantum Inf. Process. 2020 doi: 10.1007/s11128-020-02863-7. [DOI] [Google Scholar]
  • 55.Gyongyosi L, Imre S. Scalable distributed gate-model quantum computers. Sci. Rep. 2020 doi: 10.1038/s41598-020-76728-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Gyongyosi L. Unsupervised quantum gate control for gate-model quantum computers. Sci. Rep. 2020 doi: 10.1038/s41598-020-67018-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Gyongyosi L. Circuit depth reduction for gate-model quantum computers. Sci. Rep. 2020 doi: 10.1038/s41598-020-67014-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Gyongyosi L, Imre S. Quantum circuit design for objective function maximization in gate-model quantum computers. Quantum Inf. Process. 2019 doi: 10.1007/s11128-019-2326-2. [DOI] [Google Scholar]
  • 59.Teplukhin A, Kendrick B, Babikov D. Solving complex eigenvalue problems on a quantum annealer with applications to quantum scattering resonances. Phys. Chem. Chem. Phys. 2020 doi: 10.1039/D0CP04272B. [DOI] [PubMed] [Google Scholar]
  • 60.Gill, S. S. et al. Quantum computing: A taxonomy, systematic review and future directions. ACM Comput. Surv. submitted (2020).
  • 61.Caleffi, M. End-to-End Entanglement Rate: Toward a Quantum Route Metric, (2017) IEEE Globecom, 10.1109/GLOCOMW.2017.8269080 (2018).
  • 62.Caleffi M. Optimal routing for quantum networks. IEEE Access. 2017 doi: 10.1109/ACCESS.2017.2763325. [DOI] [Google Scholar]
  • 63.Caleffi, M. Cacciapuoti, A. S. & Bianchi, G. Quantum internet: from communication to distributed computing. In NANOCOM ’18: Proceedings of the 5th ACM International Conference on Nanoscale Computing and Communication (2018).
  • 64.Castelvecchi, D. The quantum internet has arrived. Nature, News and Comment. https://www.nature.com/articles/d41586-018-01835-3 (2018). [DOI] [PubMed]
  • 65.Cacciapuoti AS, et al. Quantum internet: networking challenges in distributed quantum computing. IEEE Netw. 2018 doi: 10.1109/MNET.001.1900092. [DOI] [Google Scholar]
  • 66.Cuomo, D., Caleffi, M. & Cacciapuoti, A. S. Towards a distributed quantum computing ecosystem. https://digital-library.theiet.org/content/journals/10.1049/iet-qtc.2020.0002, 10.1049/iet-qtc.2020.0002 (2020).
  • 67.Gyongyosi L, Imre S. Training optimization for gate-model quantum neural networks. Sci. Rep. 2019 doi: 10.1038/s41598-019-48892-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Van Meter R, Ladd TD, Munro WJ, Nemoto K. System design for a long-line quantum repeater. IEEE/ACM Trans. Netw. 2009;17(3):1002–1013. doi: 10.1109/TNET.2008.927260. [DOI] [Google Scholar]
  • 69.Van Meter R, Satoh T, Ladd TD, Munro WJ, Nemoto K. Path selection for quantum repeater networks. Netw. Sci. 2013;3(1–4):82–95. doi: 10.1007/s13119-013-0026-2. [DOI] [Google Scholar]
  • 70.Pant M, et al. Routing entanglement in the quantum internet. npj Quantum Inf. 2019;5:25. doi: 10.1038/s41534-019-0139-x. [DOI] [Google Scholar]
  • 71.Chakraborty, K., Rozpedeky, F., Dahlbergz, A. & Wehner, S. Distributed routing in a quantum internet. arXiv:1907.11630v1 (2019).
  • 72.Khatri S, Matyas CT, Siddiqui AU, Dowling JP. Practical figures of merit and thresholds for entanglement distribution in quantum networks. Phys. Rev. Res. 2019;1:023032. doi: 10.1103/PhysRevResearch.1.023032. [DOI] [Google Scholar]
  • 73.Khatri, S. Policies for elementary link generation in quantum networks. arXiv:2007.03193 (2020).
  • 74.Kozlowski, W. & Wehner, S. Towards large-scale quantum networks. In Proceedings of the Sixth Annual ACM International Conference on Nanoscale Computing and Communication, Dublin, Ireland, arXiv:1909.08396 (2019).
  • 75.Pathumsoot P, et al. Modeling of measurement-based quantum network coding on IBMQ devices. Phys. Rev. A. 2020;101:052301. doi: 10.1103/PhysRevA.101.052301. [DOI] [Google Scholar]
  • 76.Pal, S., Batra, P., Paterek, T. & Mahesh, T. S. Experimental localisation of quantum entanglement through monitored classical mediator. arXiv:1909.11030v1 (2019).
  • 77.Miguel-Ramiro J, Dur W. Delocalized information in quantum networks. New J. Phys. 2020 doi: 10.1088/1367-2630/ab784d. [DOI] [Google Scholar]
  • 78.Miguel-Ramiro, J., Pirker, A. & Dur, W. Genuine quantum networks: superposed tasks and addressing. arXiv:2005.00020v1 (2020).
  • 79.Pirker A, Dur W. A quantum network stack and protocols for reliable entanglement-based networks. New J. Phys. 2019;21:033003. doi: 10.1088/1367-2630/ab05f7. [DOI] [Google Scholar]
  • 80.Shannon, K., Towe, E. & Tonguz, O. On the Use of Quantum Entanglement in Secure Communications: A Survey. arXiv:2003.07907 (2020).
  • 81.Amoretti M, Carretta S. Entanglement verification in quantum networks with tampered nodes. IEEE J. Sel. Areas Commun. 2020 doi: 10.1109/JSAC.2020.2967955. [DOI] [Google Scholar]
  • 82.Cao Y, et al. Multi-tenant provisioning for quantum key distribution networks with heuristics and reinforcement learning: a comparative study. IEEE Trans. Netw. Serv. Manag. 2020 doi: 10.1109/TNSM.2020.2964003. [DOI] [Google Scholar]
  • 83.Cao Y, et al. Key as a service (KaaS) over quantum key distribution (QKD)-integrated optical networks. IEEE Commun. Mag. 2019 doi: 10.1109/MCOM.2019.1701375. [DOI] [Google Scholar]
  • 84.Liu, Y. Preliminary Study of Connectivity for Quantum Key Distribution Network. arXiv:2004.11374v1 (2020).
  • 85.Sun F. Performance analysis of quantum channels. Quantum Eng. 2020 doi: 10.1002/que2.35. [DOI] [Google Scholar]
  • 86.Chai G, et al. Blind channel estimation for continuous-variable quantum key distribution. Quantum Eng. 2020 doi: 10.1002/que2.37. [DOI] [Google Scholar]
  • 87.Ahmadzadegan, A. Learning to Utilize Correlated Auxiliary Classical or Quantum Noise. arXiv:2006.04863v1 (2020).
  • 88.Bausch, J. Recurrent Quantum Neural Networks. arXiv:2006.14619v1 (2020).
  • 89.Xin, T. Improved Quantum State Tomography for Superconducting Quantum Computing Systems, arXiv:2006.15872v1 (2020).
  • 90.Dong K, et al. Distributed subkey-relay-tree-based secure multicast scheme in quantum data center networks. Opt. Eng. 2020;59(6):065102. doi: 10.1117/1.OE.59.6.065102. [DOI] [Google Scholar]
  • 91.Amer, O., Krawec, W. O. & Wang, B. Efficient Routing for Quantum Key Distribution Networks. arXiv:2005.12404 (2020).
  • 92.Krisnanda T, et al. Probing quantum features of photosynthetic organisms. npj Quantum Inf. 2018;4:60. doi: 10.1038/s41534-018-0110-2. [DOI] [Google Scholar]
  • 93.Krisnanda T, et al. Revealing nonclassicality of inaccessible objects. Phys. Rev. Lett. 2017;119:120402. doi: 10.1103/PhysRevLett.119.120402. [DOI] [PubMed] [Google Scholar]
  • 94.Krisnanda T, et al. Observable quantum entanglement due to gravity. npj Quantum Inf. 2020;6:12. doi: 10.1038/s41534-020-0243-y. [DOI] [Google Scholar]
  • 95.Krisnanda T, et al. Detecting nondecomposability of time evolution via extreme gain of correlations. Phys. Rev. A. 2018;98:052321. doi: 10.1103/PhysRevA.98.052321. [DOI] [Google Scholar]
  • 96.Krisnanda, T. Distribution of quantum entanglement: Principles and applications. Ph.D. Dissertation, Nanyang Technological University. arXiv:2003.08657 (2020).
  • 97.Ghosh, S. et al. Universal quantum reservoir computing. arXiv:2003.09569 (2020).
  • 98.Mewes L, et al. Energy relaxation pathways between light-matter states revealed by coherent two-dimensional spectroscopy. Commun. Phys. 2020;3:157. doi: 10.1038/s42005-020-00424-z. [DOI] [Google Scholar]
  • 99.Kopszak P, Mozrzymas M, Studzinski M. Positive maps from irreducibly covariant operators. J. Phys. A Math. Theor. 2020;53:395306. doi: 10.1088/1751-8121/abaa04. [DOI] [Google Scholar]
  • 100.Guo D, et al. Comprehensive high-speed reconciliation for continuous-variable quantum key distribution. Quantum Inf. Process. 2020;19:320. doi: 10.1007/s11128-020-02832-0. [DOI] [Google Scholar]
  • 101.Chen L, Hu M. Locally maximally mixed states. Quantum Inf. Process. 2020;19:305. doi: 10.1007/s11128-020-02804-4. [DOI] [Google Scholar]
  • 102.Barbeau, M. et al. Capacity Requirements in Networks of Quantum Repeaters and Terminals. In Proceedings of IEEE International Conference on Quantum Computing and Engineering (QCE 2020) (2020).
  • 103.Yin J, et al. Entanglement-based secure quantum cryptography over 1,120 kilometres. Nature. 2020;582:501. doi: 10.1038/s41586-020-2401-y. [DOI] [PubMed] [Google Scholar]
  • 104.Santra, S. & Malinovsky, V. S. Quantum networking with short-range entanglement assistance. arXiv:2008.05553 (2020).
  • 105.Komarova K, et al. Quantum device emulates dynamics of two coupled oscillators. J. Phys. Chem. Lett. 2020 doi: 10.1021/acs.jpclett.0c01880. [DOI] [PubMed] [Google Scholar]
  • 106.Gattuso H, et al. Massively parallel classical logic via coherent dynamics of an ensemble of quantum systems with dispersion in size. ChemRxiv. 2020 doi: 10.26434/chemrxiv.12370538.v1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Chessa, S. & Giovannetti, V. Multi-level amplitude damping channels: quantum capacity analysis. arXiv:2008.00477 (2020).
  • 108.Pozzi, M. G. et al. Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers. arXiv:2007.15957 (2020).
  • 109.Bartkiewicz K, et al. Experimental kernel-based quantum machine learning in finite feature space. Sci. Rep. 2020;10:12356. doi: 10.1038/s41598-020-68911-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Kozlowski, W., Dahlberg, A. & Wehner, S. Designing a Quantum Network Protocol. arXiv:2010.02575 (2020).
  • 111.Khan, T. M. & Robles-Kelly, A. A derivative-free method for quantum perceptron training in multi-layered neural networks. ICONIP2020 (2020).
  • 112.Mehic M, et al. Quantum key distribution: a networking perspective. ACM Comput. Surv. 2020 doi: 10.1145/3402192. [DOI] [Google Scholar]
  • 113.Kao J, Chou C. Entangling capacities and the geometry of quantum operations. Sci. Rep. 2020;10:15978. doi: 10.1038/s41598-020-72881-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 114.Bae J, et al. Quantum circuit optimization using quantum Karnaugh map. Sci. Rep. 2020;10:15651. doi: 10.1038/s41598-020-72469-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Bugu, S., Ozaydin, F. & Kodera, T. Surpassing the Classical Limit in Magic Square Game with Distant Quantum Dots Coupled to Optical Cavities. arXiv:2011.01490 (2020). [DOI] [PMC free article] [PubMed]
  • 116.Welland I, Ferry DK. Wavepacket phase-space quantum Monte Carlo method. J. Comput. Electron. 2020 doi: 10.1007/s10825-020-01602-6. [DOI] [Google Scholar]
  • 117.Ferguson, M. S., Zilberberg, O. & Blatter, G. Open quantum systems beyond Fermi’s golden rule: Diagrammatic expansion of the steady-state time-convolutionless master equation. arXiv:2010.09838 (2020).
  • 118.Villalba-Diez J, Zheng X. Quantum strategic organizational design: alignment in industry 4.0 complex-networked cyber-physical lean management systems. Sensors. 2020;20:5856. doi: 10.3390/s20205856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 119.Li, S. et al. Implementing Unitary Operators with Decomposition into Diagonal Matrices of Transform Domains. arXiv:2011.03250 (2020).
  • 120.Xin T. Improved quantum state tomography for the systems with XX+YY couplings and Z readouts. Phys. Rev. A. 2020;102:052410. doi: 10.1103/PhysRevA.102.052410. [DOI] [Google Scholar]
  • 121.Pereg, U., Deppe, C. & Boche, H. Quantum Broadcast Channels with Cooperating Decoders: An Information-Theoretic Perspective on Quantum Repeaters. arXiv:2011.09233 (2020).
  • 122.Gao YL, et al. A novel quantum blockchain scheme base on quantum entanglement and DPoS. Quantum Inf. Process. 2020;19:420. doi: 10.1007/s11128-020-02915-y. [DOI] [Google Scholar]
  • 123.Bacsardi L. On the way to quantum-based satellite communication. IEEE Commun. Mag. 2013;51(08):50–55. doi: 10.1109/MCOM.2013.6576338. [DOI] [Google Scholar]
  • 124.Noelleke C, et al. Efficient teleportation between remote single-atom quantum memories. Phys. Rev. Lett. 2013;110:140403. doi: 10.1103/PhysRevLett.110.140403. [DOI] [PubMed] [Google Scholar]
  • 125.Kok P, et al. Linear optical quantum computing with photonic qubits. Rev. Mod. Phys. 2007;79:135–174. doi: 10.1103/RevModPhys.79.135. [DOI] [Google Scholar]
  • 126.Muralidharan S, Kim J, Lutkenhaus N, Lukin MD, Jiang L. Ultrafast and fault-tolerant quantum communication across long distances. Phys. Rev. Lett. 2014;112:250501. doi: 10.1103/PhysRevLett.112.250501. [DOI] [PubMed] [Google Scholar]
  • 127.Notzel, J. & DiAdamo, S. Entanglement-enhanced communication networks. In IEEE International Conference on Quantum Computing and Engineering (QCE). 10.1109/QCE49297.2020.00038 (2020).
  • 128.Wereszczynski, K. et al. Cosine series quantum sampling method with applications in signal and image processing. arXiv:2011.12738v1 (2020).
  • 129.Dai, W. Quantum Networks: State Transmission and Network Operation, PhD Dissertation, MIT (2020).
  • 130.Qian, Z. & Tsui, C. Y. A Thermal Aware Routing Algorithm for Application-Specific Network-on-Chip. In: Palesi, M. and Daneshtalab, M. (Editors) Routing Algorithms in Networks-on-Chip, Springer, ISBN 978-1-4614-8273-4, ISBN 978-1-4614-8274-1 (eBook) (2014).
  • 131.Palesi M, Holsmark R, Kumar S, Catania V. Application specific routing algorithms for networks on chip. IEEE Trans. Parallel Distrib. Syst. 2009;20(3):316–330. doi: 10.1109/TPDS.2008.106. [DOI] [Google Scholar]
  • 132.Duato J. A necessary and sufficient condition for deadlock-free adaptive routing in wormhole networks. IEEE Trans. Parallel Distrib. Syst. 1995;6(10):1055–1067. doi: 10.1109/71.473515. [DOI] [Google Scholar]
  • 133.Tarjan R. Depth-first search and linear graph algorithms. SIAM J. Comput. 1972;1(2):146–160. doi: 10.1137/0201010. [DOI] [Google Scholar]
  • 134.Chen, K. C., Chao, C. H., Lin, S. Y. & Wu, A. Y. Traffic- and Thermal-Aware Routing Algorithms for 3D Network-on-Chip (3D NoC) Systems. In: Palesi, M. and Daneshtalab, M. (Editors) Routing Algorithms in Networks-on-Chip, Springer, ISBN 978-1-4614-8273-4, ISBN 978-1-4614-8274-1 (eBook) (2014).
  • 135.Zhang-Shen, R. & McKeown, N. Designing a predictable internet backbone with valiant load-balancing. In Proceeding of Workshop of Quality of Service (IWQoS)2005 (2005).
  • 136.Zhang-Shen, R. & McKeown, B. Designing a predictable internet backbone network. In Third Workshop on Hot Topics in Networks (HotNets-III) (2004).
  • 137.Gyongyosi L, Imre S, Nguyen HV. A survey on quantum channel capacities. IEEE Commun. Surv. Tutor. 2018 doi: 10.1109/COMST.2017.2786748. [DOI] [Google Scholar]
  • 138.Van Meter R, Devitt SJ. Local and distributed quantum computation. IEEE Comput. 2016;49(9):31–42. doi: 10.1109/MC.2016.291. [DOI] [Google Scholar]
  • 139.Rozpedek F, et al. Optimizing practical entanglement distillation. Phys. Rev. A. 2018;97:062333. doi: 10.1103/PhysRevA.97.062333. [DOI] [Google Scholar]
  • 140.Liao S-K, et al. Satellite-to-ground quantum key distribution. Nature. 2017;549:43–47. doi: 10.1038/nature23655. [DOI] [PubMed] [Google Scholar]
  • 141.Ren J-G, et al. Ground-to-satellite quantum teleportation. Nature. 2017;549:70–73. doi: 10.1038/nature23675. [DOI] [PubMed] [Google Scholar]
  • 142.Hensen, B. et al. Loophole-free Bell inequality violation using electron spins separated by 1.3 kilometres. Nature526 (2015). [DOI] [PubMed]
  • 143.Hucul, D. et al. Modular entanglement of atomic qubits using photons and phonons. Nat. Phys.11(1) (2015).
  • 144.Sangouard N, et al. Quantum repeaters based on atomic ensembles and linear optics. Rev. Mod. Phys. 2011;83:33. doi: 10.1103/RevModPhys.83.33. [DOI] [Google Scholar]
  • 145.Humphreys, P. et al. Deterministic delivery of remote entanglement on a quantum network. Nature558 (2018). [DOI] [PubMed]
  • 146.Gyongyosi L, Imre S. A survey on quantum computing technology. Comput. Sci. Rev. 2018 doi: 10.1016/j.cosrev.2018.11.002. [DOI] [Google Scholar]
  • 147.Biamonte J, et al. Quantum machine learning. Nature. 2017;549:195–202. doi: 10.1038/nature23474. [DOI] [PubMed] [Google Scholar]
  • 148.Lloyd, S., Mohseni, M. & Rebentrost, P. Quantum algorithms for supervised and unsupervised machine learning. arXiv:1307.0411 (2013).
  • 149.Lloyd, S. & Weedbrook, C. Quantum generative adversarial learning. Phys. Rev. Lett. 121. arXiv:1804.09139 (2018). [DOI] [PubMed]
  • 150.Sheng YB, Zhou L. Distributed secure quantum machine learning. Sci. Bull. 2017;62:1019–1025. doi: 10.1016/j.scib.2017.06.007. [DOI] [PubMed] [Google Scholar]
  • 151.Imre S, Gyongyosi L. Advanced Quantum Communications—An Engineering Approach. New York: Wiley-IEEE Press; 2013. [Google Scholar]
  • 152.Petz D. Quantum Information Theory and Quantum Statistics. Heidelberg: Springer; 2008. [Google Scholar]
  • 153.Lloyd S. Capacity of the noisy quantum channel. Phys. Rev. A. 1997;55:1613–1622. doi: 10.1103/PhysRevA.55.1613. [DOI] [Google Scholar]
  • 154.Gisin N, Thew R. Quantum communication. Nat. Photon. 2007;1:165–171. doi: 10.1038/nphoton.2007.22. [DOI] [Google Scholar]
  • 155.Leung, D., Oppenheim, J. & Winter, A. IEEE Trans. Inf. Theory56, 3478–90 (2010).
  • 156.Quantum Internet Research Group (QIRG), web: https://datatracker.ietf.org/rg/qirg/about/(2018).
  • 157.Chou C, et al. Functional quantum nodes for entanglement distribution over scalable quantum networks. Science. 2007;316(5829):1316–1320. doi: 10.1126/science.1140300. [DOI] [PubMed] [Google Scholar]
  • 158.Yuan, Z. et al.Nature454, 1098–1101 (2008). [DOI] [PubMed]
  • 159.Schoute, E., Mancinska, L., Islam, T., Kerenidis, I. & Wehner, S. Shortcuts to quantum network routing. arXiv:1610.05238 (2016).
  • 160.Zhang W, et al. Quantum secure direct communication with quantum memory. Phys. Rev. Lett. 2017;118:220501. doi: 10.1103/PhysRevLett.118.220501. [DOI] [PubMed] [Google Scholar]
  • 161.Enk SJ, Cirac JI, Zoller P. Photonic channels for quantum communication. Science. 1998;279:205–208. doi: 10.1126/science.279.5348.205. [DOI] [PubMed] [Google Scholar]
  • 162.Briegel HJ, Dur W, Cirac JI, Zoller P. Quantum repeaters: the role of imperfect local operations in quantum communication. Phys. Rev. Lett. 1998;81:5932–5935. doi: 10.1103/PhysRevLett.81.5932. [DOI] [Google Scholar]
  • 163.Dur W, Briegel HJ, Cirac JI, Zoller P. Quantum repeaters based on entanglement purification. Phys. Rev. A. 1999;59:169–181. doi: 10.1103/PhysRevA.59.169. [DOI] [Google Scholar]
  • 164.Duan LM, Lukin MD, Cirac JI, Zoller P. Long-distance quantum communication with atomic ensembles and linear optics. Nature. 2001;414:413–418. doi: 10.1038/35106500. [DOI] [PubMed] [Google Scholar]
  • 165.Van Loock P, et al. Hybrid quantum repeater using bright coherent light. Phys. Rev. Lett. 2006;96:240501. doi: 10.1103/PhysRevLett.96.240501. [DOI] [PubMed] [Google Scholar]
  • 166.Zhao B, Chen ZB, Chen YA, Schmiedmayer J, Pan JW. Robust creation of entanglement between remote memory qubits. Phys. Rev. Lett. 2007;98:240502. doi: 10.1103/PhysRevLett.98.240502. [DOI] [PubMed] [Google Scholar]
  • 167.Goebel AM, et al. Multistage entanglement swapping. Phys. Rev. Lett. 2008;101:080403. doi: 10.1103/PhysRevLett.101.080403. [DOI] [PubMed] [Google Scholar]
  • 168.Simon C, et al. Quantum repeaters with photon pair sources and multimode memories. Phys. Rev. Lett. 2007;98:190503. doi: 10.1103/PhysRevLett.98.190503. [DOI] [PubMed] [Google Scholar]
  • 169.Tittel W, et al. Photon-echo quantum memory in solid state systems. Laser Photon. Rev. 2009;4:244–267. doi: 10.1002/lpor.200810056. [DOI] [Google Scholar]
  • 170.Sangouard N, Dubessy R, Simon C. Quantum repeaters based on single trapped ions. Phys. Rev. A. 2009;79:042340. doi: 10.1103/PhysRevA.79.042340. [DOI] [Google Scholar]
  • 171.Dur W, Briegel HJ. Entanglement purification and quantum error correction. Rep. Prog. Phys. 2007;70:1381–1424. doi: 10.1088/0034-4885/70/8/R03. [DOI] [Google Scholar]
  • 172.Lloyd S, Mohseni M, Rebentrost P. Quantum principal component analysis. Nat. Phys. 2014;10:631. doi: 10.1038/nphys3029. [DOI] [Google Scholar]
  • 173.Lloyd, S. The Universe as Quantum Computer, A Computable Universe: Understanding and exploring Nature as computation, Zenil, H. ed., World Scientific, Singapore. arXiv:1312.4455v1 (2013).
  • 174.Shor PW. Scheme for reducing decoherence in quantum computer memory. Phys. Rev. A. 1995;52:R2493–R2496. doi: 10.1103/PhysRevA.52.R2493. [DOI] [PubMed] [Google Scholar]
  • 175.Kobayashi, H., Le Gall, F., Nishimura, H. & Rotteler, M. General scheme for perfect quantum network coding with free classical communication, Lecture Notes in Computer Science (Automata, Languages and Programming SE-52 vol. 5555, Springer) 622–633 (2009).
  • 176.Hayashi M. Prior entanglement between senders enables perfect quantum network coding with modification. Phys. Rev. A. 2007;76:040301(R). doi: 10.1103/PhysRevA.76.040301. [DOI] [Google Scholar]
  • 177.Hayashi, M., Iwama, K., Nishimura, H., Raymond, R. & Yamashita, S, Quantum network coding, Lecture Notes in Computer Science (STACS 2007 SE52 vol. 4393) ed Thomas, W. and Weil, P. (Berlin Heidelberg: Springer) (2007).
  • 178.Kobayashi, H., Le Gall, F., Nishimura, H. & Rotteler, M. Perfect quantum network communication protocol based on classical network coding, Proceedings of 2010 IEEE International Symposium on Information Theory (ISIT) 2686-90. (2010).
  • 179.Chen L, Hayashi M. Multicopy and stochastic transformation of multipartite pure states. Phys. Rev. A. 2011;83(2):022331. doi: 10.1103/PhysRevA.83.022331. [DOI] [Google Scholar]
  • 180.Xiao YF, Gong Q. Optical microcavity: from fundamental physics to functional photonics devices. Sci. Bull. 2016;61:185–186. doi: 10.1007/s11434-016-0996-z. [DOI] [Google Scholar]
  • 181.Mitzenmacher N, Upfal E. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge: Cambridge University Press; 2005. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1. (148.6KB, pdf)

Data Availability Statement

This work does not have any experimental data.


Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES