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

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

QUBO-Based Scheduling of Heterogeneous Quantum Computing Resources in Scale-Free Industrial Networks

Yong Wang, Xiaoling Wang, Yu Pan, Qing Gao

Published May 1, 2026• 0 citations
Publisher
DOI
10.1109/yac71005.2026.11616218

Related papers

Quantum Optimization for Airline Itinerary and Scheduling: A QUBO and QAOA Approach

Vaibhav V. Khedekar

Abstract Airline itinerary and scheduling represent some of the most difficult computational problems that exist in transportation. The problems require choosing the best flight routes while determining schedules and allocating resources through various constraints which include regulatory requirements and maintenance schedules and dynamic demand patterns. The solution of small and medium-scale instances has been achieved through mixed-integer programming and heuristic methods including simulated annealing and genetic algorithms and branch-and-price but these methods become impractical for larger real-world instances due to exponential scaling issues. Quantum computing has introduced fresh solutions which enable the resolution of these problems. Quantum mechanical phenomena particularly superposition and entanglement enable optimization algorithms to examine an exponentially expanded solution space simultaneously. Our research applies a basic airline scheduling problem to the Traveling Salesman Problem (TSP) before converting it into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The Quantum Approximate Optimization Algorithm (QAOA) serves as a hybrid quantum-classical method to solve this formulation. The experimental evaluation of a 4-city scenario shows that quantum-based optimization generates performance levels comparable to traditional methods and indicates that improved quantum hardware development will provide computational benefits for complex airline scheduling problems. This research holds dual importance because it can minimize operational expenses and improve scheduling performance and it establishes fundamental principles for complex combinatorial problem optimization across the transportation industry. The research establishes fundamental principles for quantum-enabled applications that will revolutionize airline operations and other transportation networks by solving critical scalability and solution quality problems.

•Apr 22, 2025•0 citations

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

Quantum Knapsack: Optimizing Resource Allocation with QAOA

Mahfouz, Zein, Bhaskaran Potty Thulasi Lakshmi, Sreedevi

Combinatorial Optimization (CO) is one of the most important areas in the field of optimization, with practical applications found in every industry, including both the private and public sectors. In recent years, it was discovered that a mathematical formulation known as QUBO (Quadratic Unconstrained Binary Optimization) problem can embrace an exceptional variety of important CO problems found in the industry. In this work, we explore the Quantum Approximate Optimization Algorithm (QAOA) as a quantum computing approach to solve the Knapsack Problem efficiently by formulating the problem as a QUBO model. Here we implemented QAOA on a quantum emulator, leveraging quantum superposition and entanglement to explore multiple solutions simultaneously. The method offers a promising route for solving large, complex scheduling and resource allocation problems that are tough for classical algorithms, with potential applications in logistics, HPC resource scheduling, and energy optimization.

•Nov 17, 2025•0 citations

Quantum-Inspired Industrial Scheduling on Nanoscale Platforms

Chun‐Cheng Lin, Zhen-Yin Annie Chen, He-Yau Kang +1 more

IEEE Nanotechnology Magazine•Jan 1, 2026•0 citations

Distributed Scheduling for Modular Quantum Computing in Cloud

Vinooth Rao Kulkarni, Vipin Chaudhary

ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC)•Jul 11, 2026•0 citations

An Empirical Evaluation of Quantum-Inspired QUBO Methods for Heterogeneous HPC Workflow Mapping and Scheduling

Aasish Kumar Sharma, Christian Boehme, Julian Martin Kunkel

Heterogeneous HPC workflow scheduling under multiple hard constraints poses a challenging combinatorial optimization problem. Classical exact solvers provide optimal solutions but often face scalability limitations, motivating interest in quantum-inspired Quadratic Unconstrained Binary Optimization (QUBO) formulations as alternative optimization paradigms. This work presents a systematic and reproducible empirical evaluation of QUBO-based scheduling methods against established classical baselines, including MILP, CP-SAT, GA, and HEFT. We evaluate three QUBO variants, single-run simulated annealing, multi-attempt annealing, and a layered QAOA-inspired schedule, together with hybrid enhancement strategies on both ground-truth validation workflows (3–4 tasks) and synthetic scaling instances (5–20 tasks). All solvers are assessed through a unified evaluation pipeline that explicitly tracks feasibility, makespan, and resource utilization under progressive constraint activation and controlled penalty sweeps. Results show that all approaches recover the expected optimal makespan on validation instances, confirming formulation correctness. However, feasibility degradation emerges for specific QUBO variants as constraint interactions intensify, particularly when communication costs are introduced. Penalty sensitivity analysis reveals a sharp feasibility threshold for QUBO-SA, where insufficient penalties consistently fail and moderate-to-strong penalties restore feasibility. Scaling experiments delineate the regimes in which classical solvers and heuristics remain robust across all tested sizes, while QUBO-SA loses feasibility beyond 15 tasks and the QAOA-inspired variant beyond 10 tasks. Overall, the study provides a clear empirical characterization of the reliability boundaries of quantum-inspired QUBO formulations for heterogeneous HPC scheduling and identifies the regimes where classical approaches remain preferable under current formulations and solver capabilities.

ISC High Performance (ISC)•Jun 1, 2026•0 citations
Abstract Airline itinerary and scheduling represent some of the most difficult computational problems that exist in transportation. The problems require choosing the best flight routes while determining schedules and allocating resources through various constraints which include regulatory requirements and maintenance schedules and dynamic demand patterns. The solution of small and medium-scale instances has been achieved through mixed-integer programming and heuristic methods including simulated annealing and genetic algorithms and branch-and-price but these methods become impractical for larger real-world instances due to exponential scaling issues. Quantum computing has introduced fresh solutions which enable the resolution of these problems. Quantum mechanical phenomena particularly superposition and entanglement enable optimization algorithms to examine an exponentially expanded solution space simultaneously. Our research applies a basic airline scheduling problem to the Traveling Salesman Problem (TSP) before converting it into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The Quantum Approximate Optimization Algorithm (QAOA) serves as a hybrid quantum-classical method to solve this formulation. The experimental evaluation of a 4-city scenario shows that quantum-based optimization generates performance levels comparable to traditional methods and indicates that improved quantum hardware development will provide computational benefits for complex airline scheduling problems. This research holds dual importance because it can minimize operational expenses and improve scheduling performance and it establishes fundamental principles for complex combinatorial problem optimization across the transportation industry. The research establishes fundamental principles for quantum-enabled applications that will revolutionize airline operations and other transportation networks by solving critical scalability and solution quality problems.
Combinatorial Optimization (CO) is one of the most important areas in the field of optimization, with practical applications found in every industry, including both the private and public sectors. In recent years, it was discovered that a mathematical formulation known as QUBO (Quadratic Unconstrained Binary Optimization) problem can embrace an exceptional variety of important CO problems found in the industry. In this work, we explore the Quantum Approximate Optimization Algorithm (QAOA) as a quantum computing approach to solve the Knapsack Problem efficiently by formulating the problem as a QUBO model. Here we implemented QAOA on a quantum emulator, leveraging quantum superposition and entanglement to explore multiple solutions simultaneously. The method offers a promising route for solving large, complex scheduling and resource allocation problems that are tough for classical algorithms, with potential applications in logistics, HPC resource scheduling, and energy optimization.
Heterogeneous HPC workflow scheduling under multiple hard constraints poses a challenging combinatorial optimization problem. Classical exact solvers provide optimal solutions but often face scalability limitations, motivating interest in quantum-inspired Quadratic Unconstrained Binary Optimization (QUBO) formulations as alternative optimization paradigms. This work presents a systematic and reproducible empirical evaluation of QUBO-based scheduling methods against established classical baselines, including MILP, CP-SAT, GA, and HEFT. We evaluate three QUBO variants, single-run simulated annealing, multi-attempt annealing, and a layered QAOA-inspired schedule, together with hybrid enhancement strategies on both ground-truth validation workflows (3–4 tasks) and synthetic scaling instances (5–20 tasks). All solvers are assessed through a unified evaluation pipeline that explicitly tracks feasibility, makespan, and resource utilization under progressive constraint activation and controlled penalty sweeps. Results show that all approaches recover the expected optimal makespan on validation instances, confirming formulation correctness. However, feasibility degradation emerges for specific QUBO variants as constraint interactions intensify, particularly when communication costs are introduced. Penalty sensitivity analysis reveals a sharp feasibility threshold for QUBO-SA, where insufficient penalties consistently fail and moderate-to-strong penalties restore feasibility. Scaling experiments delineate the regimes in which classical solvers and heuristics remain robust across all tested sizes, while QUBO-SA loses feasibility beyond 15 tasks and the QAOA-inspired variant beyond 10 tasks. Overall, the study provides a clear empirical characterization of the reliability boundaries of quantum-inspired QUBO formulations for heterogeneous HPC scheduling and identifies the regimes where classical approaches remain preferable under current formulations and solver capabilities.