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

arXiv:2610.14126 · IEEE Open Journal of the Communications Society

Quantum Annealing for Resource Allocation in Next-Generation Wireless Communication Networks: A QUBO Modeling Guide

Nguyen Xuan Tung, Le Tung Giang, Mai Dinh Cong, Pham Dang Anh Duc, Nguyễn Hoàng Việt, Trinh Van Chien, Lajos Hanzo

Published Jan 1, 2026• 0 citations
arXivHTMLPDF

Abstract

Quantum annealing (QA) offers a physics-inspired approach for such subproblems by mapping them onto an Ising Hamiltonian, which is algebraically equivalent to a Quadratic Unconstrained Binary Optimization (QUBO) model executable on quantum annealers or hybrid solvers. However, their practical employment in wireless systems critically depends on the specific problem formulation, on the particular variable encoding, constraint handling, solver selection, hardware embedding, and fair benchmarking. This paper develops wireless-oriented QUBO modeling guidelines for QA-assisted resource allocation. We first present a beginner-friendly toy example in which a terrestrial-aerial resource-allocation problem with a fractional signal-to-interference-plus-noise ratio (SINR) maximization objective is reformulated into a two-bit QUBO via Boolean interpolation, converted into an Ising representation, and decoded back into a wireless scheduling decision. The same scheduling is applicable to UAV, aircraft, satellite, and terrestrial links in SAGIN systems. We then review the QA foundations and solver ecosystems, including binary quadratic, discrete quadratic, constrained quadratic, and hybrid models from the perspective of NG wireless optimization. Furthermore, we discuss both binary and discrete encoding, penalty-based constraint conversion, embedding overhead, solution decoding, and feasibility verification. A QUBO Compatibility Index is introduced as a lightweight diagnostic metric for assessing the associated QUBO formulation readiness, rather than a predictor of runtime or solution quality. Finally, a case study on max-min uplink power control in cell-free massive MIMO illustrates how a fairness-oriented wireless problem can be discretized, constrained, converted into the concept of a QUBO, and evaluated against classical baselines. A host of key challenges and future directions are also discussed.

DOI
10.1109/ojcoms.2026.3743851

Related papers

Formulations for Quantum Annealed Project Scheduling

U Kanade

Resource constrained project scheduling and related allocation tasks can be expressed as Quadratic Unconstrained Binary Optimization (QUBO) models and submitted to quantum annealers. This paper develops the QUBO formulation in detail: choice of binary variables, linear and quadratic objective terms, conversion of precedence and resource constraints into penalty functions, and calibration of penalty weights. The resulting Hamiltonian is embedded onto quantum hardware, annealed, and post-processed. Hybrid classical–quantum workflows that wrap the QUBO solver inside Monte Carlo risk analysis or a digital twin are examined. Supporting process flowcharts and energy-landscape figures illustrate every stage. The treatment remains grounded in construction and logistics applications while remaining transferable to other combinatorial domains such as ULD configuration. Keywords: QUBO formulation; quantum annealing; Ising model; penalty methods; resource-constrained project scheduling; hybrid quantum-classical; minor embedding; Monte Carlo; digital twin.

International Journal of Creative and Open Research in Engineering and Management•Aug 22, 2026•0 citations

Quantum Annealing for Optimizing Unit Scheduling in Renewable Energy Systems: Formulation and Evaluation

Sven Müller, M. Dukalski, Frank Phillipson

This paper investigates the use of Quantum Annealing (QA) devices for optimizing unit scheduling in renewable energy systems, addressing the challenge of energy balancing with intermittent generation. We compare two approaches for encoding inequality constraints in Quadratic Unconstrained Binary Optimization (QUBO) formulations: (a) the conventional slack-variable-based method and (b) a novel soft-encoding technique called unbalanced penalization. This first application of unbalanced penalization in the energy and power systems domain, aims to disseminate knowledge about near-term applications of quantum-computing and how to make best use of it within the community. Through computational experiments, we find thatUnbalanced Penalizationconsistently outperforms the conventional method of using slack variables by reducing the number of variables in the QUBO formulation. Despite this, its inherent nature penalizes feasible solutions unevenly, preventing the ability to handle much larger problem sizes. More broadly, we observe that both methods-like most pure quantum approaches-struggle with scalability on current QA hardware, as solution quality declines with increasing problem size. In light of these limitations, we assess the feasibility of purely quantum solutions and argue that hybrid approaches remain the most practical path forward.

IEEE Transactions on Power Systems•Aug 21, 2025•3 citations

A Simple Introduction to Quantum Annealing in Antennas and Propagation

Marco Donald Migliore

The objective of this paper is to introduce the application of quantum annealing (QA) to electromagnetic (EM) engineering. We demonstrate that numerous EM design and inverse problems naturally admit a mathematical formulation in terms of binary quadratic interactions. By utilizing didactic, simplified examples, we illustrate how this underlying physical structure allows for a direct mapping of EM problems onto Quadratic Unconstrained Binary Optimization (QUBO) models and equivalent Ising Hamiltonians. All numerical experiments are conducted using Simulated Annealing as a classical proxy, since the primary contribution of this work is the QUBO formulation itself rather than its execution on quantum hardware. Our results suggest that a broad class of EM problems—including array thinning, reconfigurable intelligent surface optimization, subarray partitioning, and electromagnetic inverse scattering—are suitable candidates for optimization using near-future quantum annealing architectures, once hardware connectivity and noise floors reach the required specifications.

Electronics•Jun 1, 2026•0 citations

Quantum-Driven Optimization for Massive MIMO Wireless Communication: Toward 5G Advanced and 6G Spectral Efficiency

Loganathan Balaji, A. Dhanalakshm

Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.

Journal of Engineering Science and Technology Review•Apr 1, 2026•0 citations

QUBO formulations for parallel machine scheduling

Frank Phillipson, Yoseph Y. Hutten

This paper investigates several Quadratic Unconstrained Binary optimisation (QUBO) formulations for the parallel machine scheduling problem P m | | C max with the goal of assessing their suitability for quantum annealing and related heuristic optimisation methods. Three QUBO models are constructed, ranging from formulations derived from classical integer and one-hot encodings to a more compact load-balancing based representation. Extensive computational experiments using Simulated Annealing (SA) and Quantum Annealing (QA) reveal substantial differences in scalability and solution quality among the formulations. The alternative load-balanced model consistently yields near-optimal makespans, typically within 1%–3% of the theoretical lower bound, highlighting its stability and effectiveness compared to the more penalty sensitive formulations, whose performance deteriorates for larger instances. Sensitivity analyses further reveal that the appropriate selection of penalty weights and problem-specific parameter tuning significantly influences both solution accuracy and computational effort in QUBO formulations. While current quantum hardware limitations restrict experimentation to small problem sizes, the findings illustrate how compact QUBO designs can better exploit quantum optimisation frameworks. The insights gained in this study provide practical modelling guidelines for future quantum scheduling research and lay the groundwork for extending QUBO-based approaches to more complex scheduling environments. • Three QUBO formulations are compared for identical parallel machine scheduling. • A compact load-balancing QUBO avoids auxiliary variables. • Load balancing yields makespans on average within 1–3%. • Both simulated and quantum annealing produce near-optimal schedules. • Penalty selection and encoding strongly affect scalability and solution quality.

Computers & Operations Research•Sep 11, 2026•0 citations

Benchmarking Quantum Annealing for a Greenhouse-Inspired Control QUBO

Hamzeh Alavirad, Maryam Bahrami Zanjani

We benchmark current annealing-based optimization workflows on a greenhouse-inspired quadratic unconstrained binary optimization problem for binary heater scheduling, where the horizon H denotes the number of hourly control decisions. For the main one-day instance (H=24), all solver outputs are decoded back into heater schedules and evaluated in the original greenhouse simulator using the same physical objective and feasibility criterion. Classical simulated annealing and path-integral simulated quantum annealing produce feasible near-optimal solutions in all repetitions, with best objectives close to the exact optimum. In contrast, the tested D-Wave Leap Hybrid BQM workflow is less reliable and does not outperform the classical baselines under 15--60~s requested time limits. Direct D-Wave QPU execution on reduced instances remains feasible in all runs and recovers the exact optimum for H=10 and H=12, but the exact-hit rate drops from 5/10 to 2/10 and then to 0/10 at H=14, with substantially higher variance than the classical baselines. The results do not indicate quantum advantage, but provide a reproducible, physically decoded benchmark that exposes the current strengths and limitations of classical, hybrid, and direct quantum annealing workflows on structured control QUBOs.

quant-pharXiv:2605.27670•May 28, 2026•0 citations
Quantum annealing (QA) offers a physics-inspired approach for such subproblems by mapping them onto an Ising Hamiltonian, which is algebraically equivalent to a Quadratic Unconstrained Binary Optimization (QUBO) model executable on quantum annealers or hybrid solvers. However, their practical employment in wireless systems critically depends on the specific problem formulation, on the particular variable encoding, constraint handling, solver selection, hardware embedding, and fair benchmarking. This paper develops wireless-oriented QUBO modeling guidelines for QA-assisted resource allocation. We first present a beginner-friendly toy example in which a terrestrial-aerial resource-allocation problem with a fractional signal-to-interference-plus-noise ratio (SINR) maximization objective is reformulated into a two-bit QUBO via Boolean interpolation, converted into an Ising representation, and decoded back into a wireless scheduling decision. The same scheduling is applicable to UAV, aircraft, satellite, and terrestrial links in SAGIN systems. We then review the QA foundations and solver ecosystems, including binary quadratic, discrete quadratic, constrained quadratic, and hybrid models from the perspective of NG wireless optimization. Furthermore, we discuss both binary and discrete encoding, penalty-based constraint conversion, embedding overhead, solution decoding, and feasibility verification. A QUBO Compatibility Index is introduced as a lightweight diagnostic metric for assessing the associated QUBO formulation readiness, rather than a predictor of runtime or solution quality. Finally, a case study on max-min uplink power control in cell-free massive MIMO illustrates how a fairness-oriented wireless problem can be discretized, constrained, converted into the concept of a QUBO, and evaluated against classical baselines. A host of key challenges and future directions are also discussed.
Resource constrained project scheduling and related allocation tasks can be expressed as Quadratic Unconstrained Binary Optimization (QUBO) models and submitted to quantum annealers. This paper develops the QUBO formulation in detail: choice of binary variables, linear and quadratic objective terms, conversion of precedence and resource constraints into penalty functions, and calibration of penalty weights. The resulting Hamiltonian is embedded onto quantum hardware, annealed, and post-processed. Hybrid classical–quantum workflows that wrap the QUBO solver inside Monte Carlo risk analysis or a digital twin are examined. Supporting process flowcharts and energy-landscape figures illustrate every stage. The treatment remains grounded in construction and logistics applications while remaining transferable to other combinatorial domains such as ULD configuration. Keywords: QUBO formulation; quantum annealing; Ising model; penalty methods; resource-constrained project scheduling; hybrid quantum-classical; minor embedding; Monte Carlo; digital twin.
This paper investigates the use of Quantum Annealing (QA) devices for optimizing unit scheduling in renewable energy systems, addressing the challenge of energy balancing with intermittent generation. We compare two approaches for encoding inequality constraints in Quadratic Unconstrained Binary Optimization (QUBO) formulations: (a) the conventional slack-variable-based method and (b) a novel soft-encoding technique called unbalanced penalization. This first application of unbalanced penalization in the energy and power systems domain, aims to disseminate knowledge about near-term applications of quantum-computing and how to make best use of it within the community. Through computational experiments, we find thatUnbalanced Penalizationconsistently outperforms the conventional method of using slack variables by reducing the number of variables in the QUBO formulation. Despite this, its inherent nature penalizes feasible solutions unevenly, preventing the ability to handle much larger problem sizes. More broadly, we observe that both methods-like most pure quantum approaches-struggle with scalability on current QA hardware, as solution quality declines with increasing problem size. In light of these limitations, we assess the feasibility of purely quantum solutions and argue that hybrid approaches remain the most practical path forward.
The objective of this paper is to introduce the application of quantum annealing (QA) to electromagnetic (EM) engineering. We demonstrate that numerous EM design and inverse problems naturally admit a mathematical formulation in terms of binary quadratic interactions. By utilizing didactic, simplified examples, we illustrate how this underlying physical structure allows for a direct mapping of EM problems onto Quadratic Unconstrained Binary Optimization (QUBO) models and equivalent Ising Hamiltonians. All numerical experiments are conducted using Simulated Annealing as a classical proxy, since the primary contribution of this work is the QUBO formulation itself rather than its execution on quantum hardware. Our results suggest that a broad class of EM problems—including array thinning, reconfigurable intelligent surface optimization, subarray partitioning, and electromagnetic inverse scattering—are suitable candidates for optimization using near-future quantum annealing architectures, once hardware connectivity and noise floors reach the required specifications.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
This paper investigates several Quadratic Unconstrained Binary optimisation (QUBO) formulations for the parallel machine scheduling problem P m | | C max with the goal of assessing their suitability for quantum annealing and related heuristic optimisation methods. Three QUBO models are constructed, ranging from formulations derived from classical integer and one-hot encodings to a more compact load-balancing based representation. Extensive computational experiments using Simulated Annealing (SA) and Quantum Annealing (QA) reveal substantial differences in scalability and solution quality among the formulations. The alternative load-balanced model consistently yields near-optimal makespans, typically within 1%–3% of the theoretical lower bound, highlighting its stability and effectiveness compared to the more penalty sensitive formulations, whose performance deteriorates for larger instances. Sensitivity analyses further reveal that the appropriate selection of penalty weights and problem-specific parameter tuning significantly influences both solution accuracy and computational effort in QUBO formulations. While current quantum hardware limitations restrict experimentation to small problem sizes, the findings illustrate how compact QUBO designs can better exploit quantum optimisation frameworks. The insights gained in this study provide practical modelling guidelines for future quantum scheduling research and lay the groundwork for extending QUBO-based approaches to more complex scheduling environments. • Three QUBO formulations are compared for identical parallel machine scheduling. • A compact load-balancing QUBO avoids auxiliary variables. • Load balancing yields makespans on average within 1–3%. • Both simulated and quantum annealing produce near-optimal schedules. • Penalty selection and encoding strongly affect scalability and solution quality.
We benchmark current annealing-based optimization workflows on a greenhouse-inspired quadratic unconstrained binary optimization problem for binary heater scheduling, where the horizon H denotes the number of hourly control decisions. For the main one-day instance (H=24), all solver outputs are decoded back into heater schedules and evaluated in the original greenhouse simulator using the same physical objective and feasibility criterion. Classical simulated annealing and path-integral simulated quantum annealing produce feasible near-optimal solutions in all repetitions, with best objectives close to the exact optimum. In contrast, the tested D-Wave Leap Hybrid BQM workflow is less reliable and does not outperform the classical baselines under 15--60~s requested time limits. Direct D-Wave QPU execution on reduced instances remains feasible in all runs and recovers the exact optimum for H=10 and H=12, but the exact-hit rate drops from 5/10 to 2/10 and then to 0/10 at H=14, with substantially higher variance than the classical baselines. The results do not indicate quantum advantage, but provide a reproducible, physically decoded benchmark that exposes the current strengths and limitations of classical, hybrid, and direct quantum annealing workflows on structured control QUBOs.