Power network optimization: a quantum approach
- URL: http://arxiv.org/abs/2212.01625v1
- Date: Sat, 3 Dec 2022 14:49:09 GMT
- Title: Power network optimization: a quantum approach
- Authors: Giuseppe Colucci, Stan van der Linde, Frank Phillipson
- Abstract summary: We show how to optimize transmission power networks with quantum annealing.
First, we define the QUBO problem for the partitioning of the network, and test the implementation on purely quantum and hybrid architectures.
We then solve the problem on the D-Wave hybrid CQM and BQM solvers, as well as on classical solvers available on Azure Quantum cloud.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Optimization of electricity surplus is a crucial element for transmission
power networks to reduce costs and efficiently use the available electricity
across the network. In this paper we showed how to optimize such a network with
quantum annealing. First, we define the QUBO problem for the partitioning of
the network, and test the implementation on purely quantum and hybrid
architectures. We then solve the problem on the D-Wave hybrid CQM and BQM
solvers, as well as on classical solvers available on Azure Quantum cloud.
Finally, we show that the hybrid approaches overperform the classical methods
in terms of quality of the solution, as the value of the objective function of
the quantum solutions is found to be always lower than with the classical
approaches across a set of different problem size.
Related papers
- Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection [3.6021182997326022]
We formulate a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for solving using quantum computationcolorblue.
The formulation aims to find communities with maximal self-sufficiency and minimal power flowing between them.
We benchmark the solution quality for multiple approaches: D-Wave's hybrid quantum-classical solvers, classicals, and a branch-and-bound solver.
arXiv Detail & Related papers (2024-07-09T11:44:58Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - A hybrid Quantum-Classical Algorithm for Mixed-Integer Optimization in Power Systems [0.0]
We present a framework for solving power system optimization problems with a Quantum Computer (QC)
Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow.
arXiv Detail & Related papers (2024-04-16T16:11:56Z) - Assessing Quantum Computing Performance for Energy Optimization in a
Prosumer Community [1.072460284847973]
"Prosumer problem" is the problem of scheduling the household loads on the basis of the user needs, the electricity prices, and the availability of local renewable energy.
Quantum computers can offer a significant breakthrough in treating this problem thanks to the intrinsic parallel nature of quantum operations.
We report on an extensive set of experiments, on simulators and real quantum hardware, for different problem sizes.
arXiv Detail & Related papers (2023-11-17T15:48:51Z) - Formulation of the Electric Vehicle Charging and Routing Problem for a
Hybrid Quantum-Classical Search Space Reduction Heuristic [0.0]
We show how to exploit multilevel carriers of quantum information -- qudits -- for the construction of constrained quantum optimization algorithms.
We propose a hybrid classical quantum strategy that allows us to sample constrained solutions while greatly reducing the search space of the problem.
arXiv Detail & Related papers (2023-06-07T13:16:15Z) - Entangled Pair Resource Allocation under Uncertain Fidelity Requirements [59.83361663430336]
In quantum networks, effective entanglement routing facilitates communication between quantum source and quantum destination nodes.
We propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee.
Our proposed model can reduce the total cost by at least 20% compared to the baseline model.
arXiv Detail & Related papers (2023-04-10T07:16:51Z) - Tactile Network Resource Allocation enabled by Quantum Annealing based
on ILP Modeling [0.0]
We propose a new framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models.
We map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization problem, which is solvable on quantum annealer (QA)
arXiv Detail & Related papers (2022-12-14T14:12:03Z) - Quantum-inspired optimization for wavelength assignment [51.55491037321065]
We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem.
Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
arXiv Detail & Related papers (2022-11-01T07:52:47Z) - DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization
in Future Networks [54.03701670739067]
We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks.
Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
arXiv Detail & Related papers (2022-09-16T02:44:52Z) - Entanglement Rate Optimization in Heterogeneous Quantum Communication
Networks [79.8886946157912]
Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond.
Recent advances led to the deployment of small- and large-scale quantum communication networks with real quantum hardware.
In quantum networks, entanglement is a key resource that allows for data transmission between different nodes.
arXiv Detail & Related papers (2021-05-30T11:34:23Z) - Cross Entropy Hyperparameter Optimization for Constrained Problem
Hamiltonians Applied to QAOA [68.11912614360878]
Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications.
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
arXiv Detail & Related papers (2020-03-11T13:52:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.