Warehouse optimization using a trapped-ion quantum processor

• URL: http://arxiv.org/abs/2411.17575v1
• Date: Tue, 26 Nov 2024 16:36:48 GMT
• Title: Warehouse optimization using a trapped-ion quantum processor
• Authors: Alexandre C. Ricardo, Gabriel P. L. M. Fernandes, Amanda G. Valério, Tiago de S. Farias, Matheus da S. Fonseca, Nicolás A. C. Carpio, Paulo C. C. Bezerra, Christine Maier, Juris Ulmanis, Thomas Monz, Celso J. Villas-Boas,
• Abstract summary: We adapt a formulation of a warehouse optimization problem specifically tailored as a binary optimization problem. We implement it in a trapped-ion quantum computer.
• Score: 30.432877421232842
• License:
• Abstract: Warehouse optimization stands as a critical component for enhancing operational efficiency within the industrial sector. By strategically streamlining warehouse operations, organizations can achieve significant reductions in logistical costs such as the necessary footprint or traveled path, and markedly improve overall workflow efficiency including retrieval times or storage time. Despite the availability of numerous algorithms designed to identify optimal solutions for such optimization challenges, certain scenarios demand computational resources that exceed the capacities of conventional computing systems. In this context, we adapt a formulation of a warehouse optimization problem specifically tailored as a binary optimization problem and implement it in a trapped-ion quantum computer.

Related papers

• Optimization Algorithm for Inventory Management on Classical, Quantum and Quantum-Hybrid Hardware [33.7054351451505]
  We focus on optimizing item allocation in warehouses that use gravity flow racks, which are designed for First In, First Out (FIFO) logistics. We introduce a novel strategy formulated as a QUBO problem, suitable for classical, quantum, and hybrid hardware implementations.
  arXiv  Detail & Related papers  (2024-11-18T17:36:45Z)
• Quantum optimization for Nonlinear Model Predictive Control [0.0]
  We propose a quantum computing approach for the solution of the NMPC optimization problem. The approach has the potential to considerably decrease the computational time and/or enhance the solution quality.
  arXiv  Detail & Related papers  (2024-10-25T10:56:42Z)
• Harnessing Inferior Solutions For Superior Outcomes: Obtaining Robust Solutions From Quantum Algorithms [0.0]
  We adapt quantum algorithms to tackle robust optimization problems. We present two innovative methods for obtaining robust optimal solutions. Theses are applied on two use cases within the energy sector.
  arXiv  Detail & Related papers  (2024-04-25T17:32:55Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization [59.45414406974091]
  We introduce a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks. We study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimize solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs.
  arXiv  Detail & Related papers  (2023-05-31T12:07:50Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Quantum-Inspired Optimization over Permutation Groups [0.2294014185517203]
  Quantum-inspired optimization (QIO) algorithms are computational techniques that emulate certain quantum mechanical effects on a classical hardware. We develop an algorithmic framework, called Perm-QIO, to tailor QIO tools to solve an arbitrary optimization problem.
  arXiv  Detail & Related papers  (2022-12-06T00:02:39Z)
• Prog-QAOA: Framework for resource-efficient quantum optimization through classical programs [0.0]
  Current quantum optimization algorithms require representing the original problem as a binary optimization problem, which is then converted into an equivalent Ising model suitable for the quantum device. We propose to design classical programs for computing the objective function and certifying the constraints, and later compile them to quantum circuits. This results in a new variant of the Quantum Approximate Optimization Algorithm (QAOA), which we name the Prog-QAOA.
  arXiv  Detail & Related papers  (2022-09-07T18:01:01Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• Bilevel Optimization for Differentially Private Optimization in Energy Systems [53.806512366696275]
  This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. The paper shows that, under a natural assumption, a bilevel model can be solved efficiently for large-scale nonlinear optimization problems.
  arXiv  Detail & Related papers  (2020-01-26T20:15:28Z)

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.