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1.0 B. Challenges in Distributed Quantum Computing |
0.8 1) Efficient Routing for Collaborative Quantum Comput- |
ers: With long-distance links in quantum networks, quantum |
0.6 repeaters have been introduced as an intermediate for com- |
municating between quantum computers. The quantum router |
1x cost 2x cost 3x cost 4x cost 5x cost architectureisdesignedtosustainentanglementoverquantum |
Cost of new QC deployment networks consisting of quantum memories coupled through |
photons [9]. The quantum router architecture is required to |
(b) Comparison among the proposed, EVF, and random models |
improve entanglement fidelity and deliver effective quantum |
Fig. 3: The cost breakdown and comparison of the stochastic |
routing while reducing memory latency across quantum and |
programming model for a resource allocation in distributed |
repeater networks. Open challenges remain to design an effi- |
quantum computing. |
cient quantum routing to control entanglement fidelity includ- |
ing long-distance links in distributed quantum computing. |
2) Coexistence of Multiple Distributed Quantum Algo- |
to account for variations in the deployment costs of the on- |
rithms: Sincetherewillbeavarietyofoptimizationproblems |
demand quantum computers. |
withdifferentstructuresinfuturenetworks,thecorresponding |
quantum algorithms required for them will be different ac- |
IV. OPPORTUNITIESANDCHALLENGES |
cordingly, from the number of qubits to the type of quantum |
A. Applications in Collaborative Optimization |
gates. For a single optimization problem, distributed quantum |
1) FutureSmartGrid: Extendingfromclassicalcomputing, computingcandividetheproblemintomultiplehomogeneous |
quantum computing can be used to enhance computational subproblems and deploy them on interconnected quantum |
approaches that support decision-making in smart grids, as computers for cooperative solutions. However, to meet the |
illustrated in [6]. The current smart grid issues also involve multi-taskandmulti-algorithmrequirements,distributedquan- |
power systems that operate on several timescales and dimen- tum computing must be equipped with the ability to handle |
sionsandmustberesolvedimmediately.Toaddressupcoming heterogeneousquantumcomputingtasks.Therefore,standard- |
smart grid concerns, distributed quantum computing is more izedprotocolsandefficientcollaborationwillbeindispensable |
feasible and promising than quantum computing. in distributed quantum computing in future complex network |
2) Future Internet of Things: Inspired by the aspects of optimizationproblems,e.g.,criticalandsecurecommunication |
acquiring Internet of Things (IoT) data and improving data in military environments. |
accuracy analysis, the article [2] presents a novel quantum 3) Adaptive Quantum Resource Allocation: According to |
computing-inspired (IoT-QCiO) optimization to optimize IoT- various applications in future networks, such as mission- |
sensor space by using quantum formalization based on quan- critical applications in military communications, distributed |
quantum computing must consider dynamic environments of discussedtheopportunitiesandchallengesofdistributedquan- |
using quantum resources and channels. To measure qubits, tum computing in future networks. |
therearepossibilitiesonentanglementofqubitsthatmayincur |
REFERENCES |
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