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 in computer science

QUBO Optimization of Electrical Grid Topologies

Iñigo Perez Delgado, Nerea Ruiz Onandi, Aitor Moreno-Fernández-de-Leceta, Alejandro Mata Ali, Gorka Pujana Sanchez

Published Nov 14, 2024• 0 citations
Publisher
DOI
10.1007/978-3-031-77738-7_36

Related papers

Q-GRID: Quantum Optimization for the Future Energy Grid

Jonas Blenninger, David Bucher, G. Cortiana +6 more

Künstliche Intell.•Aug 5, 2024•15 citations

Quantum-Classical Hybrid Optimization for 500-Node Power Grid Stability: Efficiency Maximization via ARK5Q-200K Protocol.

Teixeira A. C

Abstract This work introduces a hybrid quantum-classical optimization architecture designed to enhance the stability and efficiency of 500-node electrical power grids. By integrating Quantum Annealing (QA) with Graph Neural Networks (GNN) under the ARK5Q-200K protocol, we resolve the Quadratic Unconstrained Binary Optimization (QUBO) state of complex grid topologies. Our results demonstrate a reduction in transmission losses between 5-10% compared to classical GNN-based benchmarks, while maintaining a Hamiltonian precision residual of 1.48 mHa. We provide a rigorous differentiation between theoretical quantum sovereignty and the constraints of current NISQ hardware, establishing a high-fidelity baseline for the management of critical planetary and orbital energy infrastructures. This framework ensures global convergence in NP-hard energy manifolds, fulfilling the requirements for advanced infrastructure resilience.

•Feb 21, 2026•0 citations

QUBO Formulation for Fault Location on Power Grid with Sparse Measurements

Eloi Gravot, Beatriz Moya, Sergio Torregrosa +5 more

Communications in computer and information science•Jan 1, 2026•0 citations

Transcendental QAOA for Global Stability in 500-Node Power Grid Topologies: A High-Fidelity Quantum Heuristic Approach

Teixeira A. C

Abstract This work presents a novel implementation of the Quantum Approximate Optimization Algorithm (QAOA) for resolving the unit commitment and loss reduction problem in complex 500-node electrical networks. By leveraging the ARK5Q-200K protocol, we map the grid topology into a Quadratic Unconstrained Binary Optimization (QUBO) formulation. Our results demonstrate that phase-synchronized transcendental mixers achieve a ground-state energy approximation with a residual gap of $< 1\%$ relative to the Held-Karp lower bound, maintaining a Hamiltonian precision of 1.48 mHa. We show that the sub-femto scale resolution of the CCristo infrastructure effectively mitigates classical convergence failures in NP-hard logistical manifolds. This framework provides the necessary stability for large-scale energy distribution in high-density orbital or planetary infrastructures, establishing a new ontological baseline for interstellar resource management.

•Feb 21, 2026•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

A study of the optimization problem on the combination of sectionalizing switches in power grid with quantum annealing

Masaya Takahashi, Nishioka, Hiroaki, Masahiro Hirai +1 more

From the perspective of global warming, efficiency improvement of power grids is a pressing issue. Power grids have many switching devices to control the flow of electricity. Since there is a slight resistance in the wires and power consumption is proportional to the square of the current, the value of power loss on the wires changes depending on the combination of switch values that change the supply path of the current. The total number of switch combinations increases exponentially with the number of switches, and various algorithms have been studied to find the optimal combination of switch values. We propose a method to capture the switch combination problem in power grids as quadratic unconstrained binary optimization (QUBO) and derive an evaluation function to solve it using quantum annealing. The result is registered as a patent P6736787 at Japanese patent office.

arXiv:2305.07899•May 13, 2023•2 citations
Abstract This work introduces a hybrid quantum-classical optimization architecture designed to enhance the stability and efficiency of 500-node electrical power grids. By integrating Quantum Annealing (QA) with Graph Neural Networks (GNN) under the ARK5Q-200K protocol, we resolve the Quadratic Unconstrained Binary Optimization (QUBO) state of complex grid topologies. Our results demonstrate a reduction in transmission losses between 5-10% compared to classical GNN-based benchmarks, while maintaining a Hamiltonian precision residual of 1.48 mHa. We provide a rigorous differentiation between theoretical quantum sovereignty and the constraints of current NISQ hardware, establishing a high-fidelity baseline for the management of critical planetary and orbital energy infrastructures. This framework ensures global convergence in NP-hard energy manifolds, fulfilling the requirements for advanced infrastructure resilience.
Abstract This work presents a novel implementation of the Quantum Approximate Optimization Algorithm (QAOA) for resolving the unit commitment and loss reduction problem in complex 500-node electrical networks. By leveraging the ARK5Q-200K protocol, we map the grid topology into a Quadratic Unconstrained Binary Optimization (QUBO) formulation. Our results demonstrate that phase-synchronized transcendental mixers achieve a ground-state energy approximation with a residual gap of <1%< 1\%<1% relative to the Held-Karp lower bound, maintaining a Hamiltonian precision of 1.48 mHa. We show that the sub-femto scale resolution of the CCristo infrastructure effectively mitigates classical convergence failures in NP-hard logistical manifolds. This framework provides the necessary stability for large-scale energy distribution in high-density orbital or planetary infrastructures, establishing a new ontological baseline for interstellar resource management.
From the perspective of global warming, efficiency improvement of power grids is a pressing issue. Power grids have many switching devices to control the flow of electricity. Since there is a slight resistance in the wires and power consumption is proportional to the square of the current, the value of power loss on the wires changes depending on the combination of switch values that change the supply path of the current. The total number of switch combinations increases exponentially with the number of switches, and various algorithms have been studied to find the optimal combination of switch values. We propose a method to capture the switch combination problem in power grids as quadratic unconstrained binary optimization (QUBO) and derive an evaluation function to solve it using quantum annealing. The result is registered as a patent P6736787 at Japanese patent office.