Quantum and quantum-inspired optimization for an in-core fuel management problem

• URL: http://arxiv.org/abs/2308.13348v1
• Date: Fri, 25 Aug 2023 12:40:19 GMT
• Title: Quantum and quantum-inspired optimization for an in-core fuel management problem
• Authors: Sergey R. Usmanov, Gleb V. Salakhov, Anton A. Bozhedarov, Evgeniy O. Kiktenko, Aleksey K. Fedorov
• Abstract summary: Operation management of nuclear power plants consists of several computationally hard problems. The main challenge of this optimization problem is the exponential growth of the search space with a number of loading elements. This work demonstrates potential applications of quantum computers and quantum-inspired algorithms in the energy industry.
• Score: 0.9642500063568188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Operation management of nuclear power plants consists of several computationally hard problems. Searching for an in-core fuel loading pattern is among them. The main challenge of this combinatorial optimization problem is the exponential growth of the search space with a number of loading elements. Here we study a reloading problem in a Quadratic Unconstrained Binary Optimization (QUBO) form. Such a form allows us to apply various techniques, including quantum annealing, classical simulated annealing, and quantum-inspired algorithms in order to find fuel reloading patterns for several realistic configurations of nuclear reactors. We present the results of benchmarking the in-core fuel management problem in the QUBO form using the aforementioned computational techniques. This work demonstrates potential applications of quantum computers and quantum-inspired algorithms in the energy industry.

Related papers

• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
  We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials. It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method. We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
  arXiv  Detail & Related papers  (2024-02-22T16:33:48Z)
• Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval [1.6421520075844793]
  A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable problem. The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems.
  arXiv  Detail & Related papers  (2024-02-09T15:01:56Z)
• 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)
• Qubit efficient quantum algorithms for the vehicle routing problem on NISQ processors [48.68474702382697]
  Vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables.
  arXiv  Detail & Related papers  (2023-06-14T13:44:35Z)
• Particle track reconstruction with noisy intermediate-scale quantum computers [0.0]
  Reconstruction of trajectories of charged particles is a key computational challenge for current and future collider experiments. The problem can be formulated as a quadratic unconstrained binary optimization (QUBO) and solved using the variational quantum eigensolver (VQE) algorithm. This work serves as a proof of principle that the VQE could be used for particle tracking and investigates modifications of the VQE to make it more suitable for optimization.
  arXiv  Detail & Related papers  (2023-03-23T13:29:20Z)
• Quantum and quantum-inspired optimization for solving the minimum bin packing problem [0.0]
  We consider the problem of filling spent nuclear fuel in deep-repository canisters that is relevant for atomic energy industry. We first redefine the aforementioned problem it in terms of quadratic unconstrained binary optimization. Results of our study indicate on the possibility to solve industry-relevant problems of atomic energy industry using quantum and quantum-inspired optimization.
  arXiv  Detail & Related papers  (2023-01-26T18:04:18Z)
• Towards optimization under uncertainty for fundamental models in energy markets using quantum computers [0.0]
  We suggest a first approach to consider uncertainties in the renewable energy supply, power demand and machine failures. We show how to find cost-saving solutions of the UCP under these uncertainties on quantum computers.
  arXiv  Detail & Related papers  (2023-01-03T14:07:15Z)
• 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)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• Adiabatic Quantum Graph Matching with Permutation Matrix Constraints [75.88678895180189]
  Matching problems on 3D shapes and images are frequently formulated as quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard. We propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware. The proposed algorithm has the potential to scale to higher dimensions on future quantum computing architectures.
  arXiv  Detail & Related papers  (2021-07-08T17:59:55Z)

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.