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arXiv:2307.13185

Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing

Rakpong Kaewpuang, Minrui Xu, Dinh Thai Hoang, Dusit Tao Niyato, Yu Han, Ruidong Li, Zehui Xiong, Jiawen Kang

Published Jul 25, 2023• 1 citation
arXivHTMLPDF

Abstract

Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources, such as quantum computers, to perform resource-intensive tasks. Like traditional cloud computing platforms, QCC providers can offer both reservation and on-demand plans for quantum resource provisioning to satisfy users' requirements. However, the fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. Furthermore, in distributed QCC, entanglement routing is a critical component of quantum networks that enables remote entanglement communication between users and QCC providers. Further, maintaining entanglement fidelity in quantum networks is challenging due to the requirement for high-quality entanglement routing, especially when accessing the providers over long distances. To address these challenges, we propose a resource allocation model to provision quantum computing and networking resources. In particular, entangled pairs, entanglement routing, qubit resources, and circuits' waiting time are jointly optimized to achieve minimum total costs. We formulate the proposed model based on the two-stage stochastic programming, which takes into account the uncertainties of fidelity and qubit requirements, and quantum circuits' waiting time. Furthermore, we apply the Benders decomposition algorithm to divide the proposed model into sub-models to be solved simultaneously. Experimental results demonstrate that our model can achieve the optimal total costs and reduce total costs at most 49.43\% in comparison to the baseline model.

DOI
10.48550/arxiv.2307.13185

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Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources, such as quantum computers, to perform resource-intensive tasks. Like traditional cloud computing platforms, QCC providers can offer both reservation and on-demand plans for quantum resource provisioning to satisfy users' requirements. However, the fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. Furthermore, in distributed QCC, entanglement routing is a critical component of quantum networks that enables remote entanglement communication between users and QCC providers. Further, maintaining entanglement fidelity in quantum networks is challenging due to the requirement for high-quality entanglement routing, especially when accessing the providers over long distances. To address these challenges, we propose a resource allocation model to provision quantum computing and networking resources. In particular, entangled pairs, entanglement routing, qubit resources, and circuits' waiting time are jointly optimized to achieve minimum total costs. We formulate the proposed model based on the two-stage stochastic programming, which takes into account the uncertainties of fidelity and qubit requirements, and quantum circuits' waiting time. Furthermore, we apply the Benders decomposition algorithm to divide the proposed model into sub-models to be solved simultaneously. Experimental results demonstrate that our model can achieve the optimal total costs and reduce total costs at most 49.43% in comparison to the baseline model.
Quantum cloud computing is a promising paradigm for efficiently provisioning quantum resources (i.e., qubits) to users. In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. Literally, the cost of quantum resources in the reservation plan is expected to be cheaper than the cost of quantum resources in the on-demand plan. However, quantum resources in the reservation plan have to be reserved in advance without information about the requirement of quantum circuits beforehand, and consequently, the resources are insufficient, i.e., under-reservation. Hence, quantum resources in the on-demand plan can be used to compensate for the unsatisfied quantum resources required. To end this, we propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized. Particularly, the objective is to minimize the total costs of quantum circuits under uncertainties regarding qubit requirement and minimum waiting time of quantum circuits. In experiments, practical circuits of quantum Fourier transform are applied to evaluate the proposed qubit resource allocation. The results illustrate that the proposed qubit resource allocation can achieve the optimal total costs.
In quantum networks, effective entanglement routing facilitates remote entanglement communication between quantum source and quantum destination nodes. Unlike routing in classical networks, entanglement routing in quantum networks must consider the quality of entanglement qubits (i.e., entanglement fidelity), presenting a challenge in ensuring entanglement fidelity over extended distances. To address this issue, we propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee. This approach jointly optimizes entangled resources (i.e., entangled pairs) and entanglement routing to support applications in quantum networks. Our proposed model is formulated using two-stage stochastic programming, taking into account the uncertainty of quantum application requirements. Aiming to minimize the total cost, our model ensures efficient utilization of entangled pairs and energy conservation for quantum repeaters under uncertain fidelity requirements. Experimental results demonstrate that our proposed model can reduce the total cost by at least 20% compared to the baseline model.
With the advent of interconnected quantum computers, i.e., distributed quantum computing (DQC), multiple quantum computers can now collaborate via quantum networks to perform massively complex computational tasks. However, DQC faces problems sharing quantum information because it cannot be cloned or duplicated between quantum computers. Thanks to advanced quantum mechanics, quantum computers can teleport quantum information across quantum networks. However, challenges to utilizing efficiently quantum resources, e.g., quantum computers and quantum channels, arise in DQC due to their capabilities and properties, such as uncertain qubit fidelity and quantum channel noise. In this paper, we propose a resource allocation scheme for DQC based on stochastic programming to minimize the total deployment cost for quantum resources. Essentially, the two-stage stochastic programming model is formulated to handle the uncertainty of quantum computing demands, computing power, and fidelity in quantum networks. The performance evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers while minimizing the overall cost of provisioning under uncertainty.
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately 5%5\%5% in execution time, 30%30\%30% in communication overhead, 40%40\%40% in wait time and 2%2\%2% in fidelity, providing better solutions to efficient allocation strategies.
Quantum cloud computing will play a vital role in achieving quantum supremacy by providing flexible allocation of resources for applications that demand extensive computational power. Such a platform allows various clients to run their quantum jobs (quantum circuits) without managing the quantum hardware and to pay based on resource usage. Therefore, defining optimal quantum resource allocation is essential to prevent overcharging clients and to help quantum cloud providers maximize resource utilization. Our approach involves examining the problem from a game theory perspective. We introduce a quantum circuit partitioning resource allocation game model (QC-PRAGM) designed to minimize client costs and inter-node communication. This is achieved by solving a convex optimization problem that finds the system's optimal cost while maximizing the number of local gates within a partition through the selection of the best qubit combinations. We demonstrate analytically that clients are charged fairly. Additionally, our simulations show that our solutions outperform traditional methods.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.