Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Variational Quantum Eigensolver for Optimizing Network Scheduling Using QUBO Formulation

Syed Muhammad Abuzar Rizvi, Muhammad Mustafa Umar Gondel, Usama Inam Paracha, Hyundong Shin

Published Jan 1, 2024• 2 citations
Publisher
DOI
10.1007/978-3-031-67357-3_4

Related papers

Energy Storage Scheduling: A QUBO Formulation for Quantum Computing

Frank Phillipson, Tariq Bontekoe, Irina Chiscop

Communications in computer and information science•Jan 1, 2021•7 citations

Enhanced QUBO Formulations for The Flow Shop Scheduling Problem

Yousra Farhani, Malak Saiem, Taha Arbaoui +1 more

In recent years, the Flow shop Scheduling Problem (FSP) has gained significant attention, prompting researchers to develop various methods, from conventional to hybrid optimization algorithms. Driven by the rapid growth of quantum computing capabilities, quantum approaches applied to optimization problems have attracted an increasing research effort. This work attempts to solve the permutation flow shop scheduling problem using quantum annealing by formulating the problem as a Quadratic Unconstrained Binary Optimization model (QUBO). Two QUBO formulations tailored to address the FSP are used. The first formulation is based on the position-based modelling while the second is based on five approximations of FSP as a Traveling Salesman Problem (TSP). The QUBO formulations are tested, using D-Wave's quantum annealers, on the well-known Taillard FSP benchmark and compared against each other. Results show that the proposed position-based QUBO reaches better solutions than the TSP-based QUBOs. This work is an attempt to highlight the increasing effectiveness of quantum annealers in addressing complex optimization problems and their limitations compared to conventional classical methods.

Genetic and Evolutionary Computation Conference Companion (GECCO Companion)•Jul 14, 2024•4 citations

Variational Quantum Eigensolver and Its Applications

Atsushi Matsuo

Lecture notes in computer science•Jan 1, 2021•1 citation

Improved optimization algorithm for use in variational quantum eigensolvers

Titus Morris

Bulletin of the American Physical Society•Mar 5, 2019•1 citation

Large-scale wireless coverage optimization: A quantum approach

Chengkang Pan, Xin Yi, Shuai Hou +5 more

Wireless network coverage optimization is critical for improving service quality. However, optimizing large-scale networks remains challenging for both classical algorithms and quantum methods in the NISQ era. This paper proposes a quantum approach that models the problem as a covering graph, partitions it using a QUBO formulation, and solves subproblems via a filtered variational quantum eigensolver. The method is experimentally validated on real quantum hardware, including a coherent Ising machine and a superconducting quantum processor, and compared with classical methods like SA and PSO. This work introduces a divide-and-conquer strategy for large-scale network coverage optimization and expands the solution landscape.

ICT Express•Jul 15, 2025•2 citations

Application of Quantum Annealing to Supply Chain Planning under Uncertainty

Jesus Garcia Garcia, Pablo Galan Jativa

Supply chain planning is a complex optimization problem that involves the coordination of multiple resources in order to reduce costs and maximize benefit. In this study, we propose a QUBO formulation for the scheduling of the transportation of materials to a factory with uncertain arrival times. The standard methods like network flow or MILP solvers escalate exponentially with the problem parameters, making computation times grow rapidly. Quantum annealing is a candidate for an optimization poccess that can solve this computationally intense problems, and thus it is important to be able to find compatible formulations. The model proposed makes use of the QUBO formulation to give optimal solutions to the scheduling with unceratinty. We will establish the problem from a stochastic optimization point of view, and then build a hamiltonian that encodes it, following the QUBO formulation. With the quick increase in qubit numbers of quantum computers, this model seems like a promising tool that will provide solutions to very relevant and computionally hard problems in the near future.

Genetic and Evolutionary Computation Conference Companion (GECCO Companion)•Jul 15, 2023•3 citations
In recent years, the Flow shop Scheduling Problem (FSP) has gained significant attention, prompting researchers to develop various methods, from conventional to hybrid optimization algorithms. Driven by the rapid growth of quantum computing capabilities, quantum approaches applied to optimization problems have attracted an increasing research effort. This work attempts to solve the permutation flow shop scheduling problem using quantum annealing by formulating the problem as a Quadratic Unconstrained Binary Optimization model (QUBO). Two QUBO formulations tailored to address the FSP are used. The first formulation is based on the position-based modelling while the second is based on five approximations of FSP as a Traveling Salesman Problem (TSP). The QUBO formulations are tested, using D-Wave's quantum annealers, on the well-known Taillard FSP benchmark and compared against each other. Results show that the proposed position-based QUBO reaches better solutions than the TSP-based QUBOs. This work is an attempt to highlight the increasing effectiveness of quantum annealers in addressing complex optimization problems and their limitations compared to conventional classical methods.
Wireless network coverage optimization is critical for improving service quality. However, optimizing large-scale networks remains challenging for both classical algorithms and quantum methods in the NISQ era. This paper proposes a quantum approach that models the problem as a covering graph, partitions it using a QUBO formulation, and solves subproblems via a filtered variational quantum eigensolver. The method is experimentally validated on real quantum hardware, including a coherent Ising machine and a superconducting quantum processor, and compared with classical methods like SA and PSO. This work introduces a divide-and-conquer strategy for large-scale network coverage optimization and expands the solution landscape.
Supply chain planning is a complex optimization problem that involves the coordination of multiple resources in order to reduce costs and maximize benefit. In this study, we propose a QUBO formulation for the scheduling of the transportation of materials to a factory with uncertain arrival times. The standard methods like network flow or MILP solvers escalate exponentially with the problem parameters, making computation times grow rapidly. Quantum annealing is a candidate for an optimization poccess that can solve this computationally intense problems, and thus it is important to be able to find compatible formulations. The model proposed makes use of the QUBO formulation to give optimal solutions to the scheduling with unceratinty. We will establish the problem from a stochastic optimization point of view, and then build a hamiltonian that encodes it, following the QUBO formulation. With the quick increase in qubit numbers of quantum computers, this model seems like a promising tool that will provide solutions to very relevant and computionally hard problems in the near future.