Entangled Pair Resource Allocation under Uncertain Fidelity Requirements

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.Comment: 6 pages and 6 figure

Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing

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.Comment: 30 pages and 20 figure

Decomposition of stochastic power management for wireless base station in smart grid

We propose a stochastic power management (SPM) algorithm for a wireless base station to optimize the power consumption (i.e., minimizing the power cost while meeting wireless traffic demand). This SPM algorithm is developed for a smart grid environment which takes a renewable power source and time-varying power price into account. An optimization model is developed to obtain an optimal solution of the SPM algorithm. This optimization model considers various uncertainties including power price, renewable power, and wireless traffic load. Benders decomposition method is applied to reduce the execution time of obtaining the optimal solution for the SPM algorithm

Evolutionary carrier selection for shared truck delivery services

With multiple carriers in a logistics market, customers can choose the best carrier to deliver their products and packages. In this paper, we present a novel approach of using the stochastic evolutionary game to analyze the decision-making of the customers using the less-than-truckload (LTL) delivery service. We propose inter-related optimization and game models that allow us to analyze the vehicle routing optimization for the LTL carriers and carrier selection for the customers, respectively. The stochastic evolutionary game model incorporates a small perturbation of customers' decision-making which exists due to irrationality. The solution of the stochastic evolutionary game in terms of stochastically stable states is characterized by using the Markov chain model. The numerical results show the impact of carriers' and customers' parameters on the stable states.Info-communications Media Development Authority (IMDA)Nanyang Technological UniversityNational Research Foundation (NRF)This work was supported in part by the National Research Foundation Singapore and DSO National Laboratories under the AI Singapore Programme under AISG Award AISG2-RP-2020-019, in part by the National Research Foundation (NRF), in part by Singapore and Infocomm Media Development Authority through the Future Communications Research Development Programme (FCP), in part by Energy Research Test-Bed and Industry Partnership Funding Initiative through Energy Grid (EG) 2.0 Programme, in part by the DesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) Programme; Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), and in part by Nanyang Assistant Professorship, Nanyang Technological University, Singapore

An energy efficient solution : integrating plug-in hybrid electric vehicle in smart grid with renewable energy

Nowadays, there is a conflict between the rapidly increasing demand for electricity and the requirement for reducing dependence on fossil fuel to decrease the greenhouse gas emissions. Proper utilization of renewable energy such as wind energy is proposed as an efficient solution to address this problem. However, due to the high inter-temporal variation and limited predictability, it is difficult to make full use of renewable energy as supplement to the conventional thermal power plants in smart grid. In this paper, we provide an energy efficient solution to solve this problem: the integration of Pug-In-Hybrid Electric Vehicle (PHEV). Through proper charging and discharging processes, the PHEV fleet can act as energy storage when there is excess renewable energy, while in case of energy shortage, the energy can be properly returned to the grid. Optimization models based on stochastic programming are developed for both the cases without PHEV fleet and with PHEV fleet, while various uncertainties such as power price, renewable power and user demand are taken into account. By solving the two stochastic programming problems, the optimal power management solutions are obtained, and the numerical results show that the integration of PHEV can effectively reduce the energy to be generated by conventional thermal power plants, and as a result, the overall energy generation cost can be significantly reduced

Adaptive power management for data center in smart grid environment

We propose an adaptive power management (APM) algorithm for a data center with an objective to minimize the total cost of power bought from an electrical grid. This APM algorithm is developed for a smart grid environment which is envisioned to be a cooperative, responsive, and economical power system. In particular, APM algorithm takes the spot power price from an electrical grid, the power supply from a renewable power source, and users' demand in terms of application workload processing into account when managing the power consumption. Therefore, an APM algorithm is considered to be the demand side management in a smart grid. To obtain an optimal decision of the APM algorithm, an optimization model based on stochastic programming with multi-stage recourse is developed. This optimization model considers various uncertainties and is able to determine the optimal solution for the APM algorithm. The APM algorithm is evaluated by numerical studies. The numerical results clearly show that the APM algorithm can minimize the power cost of a data center