Optical experimental solution for the multiway number partitioning problem and its application to computing power scheduling

Wen, Jingwei; Wang, Zhenming; Huang, Zhiguo; Cai, Dunbo; Jia, Bingjie; Cao, C; Yin, Ming; Wei, Hai; Wen, Kai; Qian, Ling

doi:10.1007/s11433-023-2147-3

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…By integrating insights from our previous research, which focused on enhancing the adversarial robustness of CVQKD systems through machine learning and compressed sensing, we aim to demonstrate the potential of QUBO models and quantum-inspired algorithms in safeguarding the integrity of quantum communication networks. This integration not only showcases the versatility of these techniques but also underscores their relevance in addressing the security challenges inherent in quantum communication, a critical component of the future of secure information exchange [9] . This paper presents a comprehensive analysis and solution framework for decision optimization in computational networks using Quantum Unweighted Quadratic Unconstrained Binary Optimization (QUBO) models.…”

Section: Introductionmentioning

confidence: 78%

Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking

Zhu,

Qiu,

Wang

2024

International Conference on Computer Network Security and Software Engineering (CNSSE 2024)

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Section: Introductionmentioning

confidence: 78%

Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking

Zhu,

Qiu,

Wang

2024

International Conference on Computer Network Security and Software Engineering (CNSSE 2024)

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“…CIM solves the Ising problem according to the principle of minimum gain, and can find the ground state or low energy state of Ising Hamiltonian. The method is to map the QUBO problem into a fully connected Ising Hamiltonian with programmable parameters, and obtain the solution of the problem through controllable quantum phase transition 33 , 34 .…”

Section: Agv Scheduling Modelmentioning

confidence: 99%

“…There are 211 oscillation pulses in the fiber ring, and the time interval between adjacent pulses is 10 ns, so the transmission time of optical pulses in the ring is 2.11 µs. Then, the laser output in the fiber ring and the laser with the fundamental frequency of 1560 nm are determined by BHD, and the FPGA obtains the feedback signal of the next round trip according to the interaction intensity between spins in Ising Hamiltonian, which is used as the control signal of the intensity modulator (IM), and its sign defines the phase shift (0 or ) of the phase modulator (PM) 9 , 14 , 34 , 38 .…”

Section: Numerical Experimentsmentioning

confidence: 99%

Quantum computing for several AGV scheduling models

Tang,

Yang,

Wen

et al. 2024

Sci Rep

Self Cite

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Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

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High-speed train timetable optimization based on space–time network model and quantum simulator

Xu,

Chen,

Zhang

et al. 2023

Quantum Inf Process

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Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space–time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.

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Optical experimental solution for the multiway number partitioning problem and its application to computing power scheduling

Cited by 8 publications

References 50 publications

Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking

Based on QUBO models with quantum-inspired algorithms to enhance the CVQKD systems to ensure security of hacking

Quantum computing for several AGV scheduling models

High-speed train timetable optimization based on space–time network model and quantum simulator

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