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2
DQC O: Distributed Quantum Computing for
Collaborative Optimization in Future Networks
Napat Ngoenriang, Minrui Xu, Jiawen Kang, Dusit Niyato, Han Yu, and Xuemin (Sherman) Shen
Abstract—With the advantages of high-speed parallel pro- The principles of quantum mechanics are used to enable
cessing, quantum computers can efficiently solve large-scale quantum bits (i.e., qubits) in quantum computers which are
complex optimization problems in future networks. However,
superior to classical computers based on binary bits. For
due to the uncertain qubit fidelity and quantum channel noise,
example, Shor’s [3] algorithm and Grover’s [4] algorithm,
2202 distributed quantum computing which relies on quantum net-
works connected through entanglement faces a lot of challenges two well-known quantum algorithms, were developed to ef-
for exchanging information across quantum computers. In this ficiently solve factorization and search for unstructured data,
paper, we propose an adaptive distributed quantum computing respectively, These tasks are highly challenging for classical
approachtomanagequantumcomputersandquantumchannels
peS computers. However, scaling up quantum computers is a key
for solving optimization tasks in future networks. Firstly, we
challenge in deploying quantum computers in practice. To
describe the fundamentals of quantum computing and its dis-
tributed concept in quantum networks. Secondly, to address the date, only a small number of qubits can be implemented
61 uncertaintyoffuturedemandsofcollaborativeoptimizationtasks in a single quantum computer. Moreover, quantum tasks
and instability over quantum networks, we propose a quantum usually require the use of multiple qubits in more complex
resourceallocationschemebasedonstochasticprogrammingfor
applications. Due to the instability of qubits and the amount
]CD.sc[ minimizing quantum resource consumption. Finally, based on
of information required, it becomes more difficult to manage
the proposed approach, we discuss the potential applications
for collaborative optimization in future networks, such as smart and control information in quantum computers with a small
grid management, IoT cooperation, and UAV trajectory plan- number of qubits. Thus, distributed quantum computing has
ning. Promising research directions that can lead to the design been proposed in an attempt to alleviate this problem [5].
and implementation of future distributed quantum computing
Theconceptofdistributedquantumcomputing,whichrefers
frameworks are also highlighted.
tomultipleinterconnectedquantumcomputers,wasintroduced
1v78820.0122:viXra Index Terms—Distributed quantum computing, quantum net-
to accelerate and perform collaboratively quantum computa-
works, resource allocation
tions through quantum networks. However, due to the prin-
ciples of quantum mechanics, qubits cannot be duplicated or
I. INTRODUCTION
cloned.Distributedquantumcomputingrequiresquantumtele-
Inthequantumera,theadvancementofquantumcomputing
portation,inwhichqubitsareteleportedbetweentwoquantum
and communication has attracted significant interest from
computers. In this way, a large, complex computational task
researchers, government organizations and the industry [1].
can be accomplished jointly by multiple quantum computers.
Quantum computers provide effective solutions to complex
It has been shown that the most commonly used quantum
optimizationproblemsinaresourceefficientmanner,afeatnot
algorithms benefit from their distributed counterparts. For
possible for classical computers to achieve. The recent adop-
example,distributedGrover’salgorithm[4]incurssignificantly
tionofquantumcomputinghasfurtherboostedtechnologyin-
shorter query time than Grover’s algorithm, and distributed
novations such as artificial intelligence (AI), intelligent traffic
Shor’s algorithm [3] is less complex than Shor’s algorithm.
monitoring, weather forecasting, improved battery chemistry,
The distributed versions of both algorithms increase their
andlife-savingpharmaceuticals[1].Theproliferationofquan-
viability of solving complex problems in practice.
tumcomputinghasalsoevolvedtoacceleratecomputationfor
Although distributed quantum computing has many advan-
various applications and services in future networks, which
tagesoverasinglequantumcomputerandclassicalcomputers,
mustbedesignedefficientlyusinglimitedresourcestoperform
designing efficient large-scale distributed quantum comput-
a wide range of heterogeneous tasks [2].
ing still faces many challenges. Firstly, the effectiveness of
using quantum computers is determined by the demand of
N. Ngoenriang is with the School of Information Science and Technol-
the applications, like military communication. This is un-
ogy, Vidyasirimedhi Institute of Science and Technology, Thailand (e-mail:
naphat.n s17@vistec.ac.th. known at the time of deployment, making planning difficult.
M. Xu, H. Yu and D. Niyato are with the School of Computer Science Secondly, the availability and computing power of quantum
andEngineering,NanyangTechnologicalUniversity,Singapore(e-mail:min-
computers, which may vary over time, also affects whether