Application of quantum annealing for scalable robotic assembly line optimization: a case study

• URL: http://arxiv.org/abs/2412.09239v1
• Date: Thu, 12 Dec 2024 12:49:34 GMT
• Title: Application of quantum annealing for scalable robotic assembly line optimization: a case study
• Authors: Moritz Willmann, Marcel Albus, Jan Schnabel, Marco Roth,
• Abstract summary: We investigate applying quantum computing to the real-world based problem of Robotic Assembly Line Balancing (RALB) We transform the integer programming formulation into a quadratic unconstrained binary optimization problem, which is then solved using a hybrid quantum-classical algorithm on the D-Wave Advantage 4.1 quantum computer. In a case study, the quantum solution is compared to an exact solution, demonstrating the potential for quantum computing to enhance manufacturing productivity and reduce costs.
• Score: 0.40498500266986387
• License:
• Abstract: The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitability, known as Robotic Assembly Line Balancing (RALB). With the increasing complexity of manufacturing required by mass customization, traditional computational approaches struggle to solve RALB problems efficiently. To address these scalability challenges, we investigate applying quantum computing, particularly quantum annealing, to the real-world based problem. We transform the integer programming formulation into a quadratic unconstrained binary optimization problem, which is then solved using a hybrid quantum-classical algorithm on the D-Wave Advantage 4.1 quantum computer. In a case study, the quantum solution is compared to an exact solution, demonstrating the potential for quantum computing to enhance manufacturing productivity and reduce costs. Nevertheless, limitations of quantum annealing, including hardware constraints and problem-specific challenges, suggest that continued advancements in quantum technology will be necessary to improve its applicability to RALB manufacturing optimization.

Related papers

• Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver [1.4769913341588448]
  We introduce conditional Generative Quantum Eigensolver (conditional-GQE), a context-aware quantum circuit generator powered by an encoder-decoder Transformer. We train our generator for solving problems with up to 10 qubits, exhibiting nearly perfect performance on new problems.
  arXiv  Detail & Related papers  (2025-01-28T14:35:46Z)
• 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)
• Performant near-term quantum combinatorial optimization [1.1999555634662633]
  We present a variational quantum algorithm for solving optimization problems with linear-depth circuits. Our algorithm uses an ansatz composed of Hamiltonian generators designed to control each term in the target quantum function. We conclude our performant and resource-minimal approach is a promising candidate for potential quantum computational advantages.
  arXiv  Detail & Related papers  (2024-04-24T18:49:07Z)
• Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm [14.554010382366302]
  Quantum Approximate Optimization (QAOA) stands out for its potential to efficiently solve the Max-Cut problem. We use Graph Neural Networks (GNN) as a warm-start technique to optimize QAOA, using GNN as a warm-start technique. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing.
  arXiv  Detail & Related papers  (2024-03-05T20:23:25Z)
• 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)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• 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)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• 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)

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.