Quantum Computing Techniques for Multi-Knapsack Problems

• URL: http://arxiv.org/abs/2301.05750v2
• Date: Thu, 28 Sep 2023 18:45:52 GMT
• Title: Quantum Computing Techniques for Multi-Knapsack Problems
• Authors: Abhishek Awasthi, Francesco B\"ar, Joseph Doetsch, Hans Ehm, Marvin Erdmann, Maximilian Hess, Johannes Klepsch, Peter A. Limacher, Andre Luckow, Christoph Niedermeier, Lilly Palackal, Ruben Pfeiffer, Philipp Ross, Hila Safi, Janik Sch\"onmeier-Kromer, Oliver von Sicard, Yannick Wenger, Karen Wintersperger, Sheir Yarkoni
• Abstract summary: We investigate some of the most prominent and state-of-the-art quantum algorithms using different quantum software and hardware tools. We consider several gate-based quantum algorithms, such as QAOA and VQE, and present an exhaustive study of the solutions and the estimation of runtimes.
• Score: 1.0136953995598361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum computing has opened a new paradigm for computationally intensive tasks, with promises of delivering better and faster solutions for specific classes of problems. This work presents a comprehensive study of quantum computing approaches for multi-knapsack problems, by investigating some of the most prominent and state-of-the-art quantum algorithms using different quantum software and hardware tools. The performance of the quantum approaches is compared for varying hyperparameters. We consider several gate-based quantum algorithms, such as QAOA and VQE, as well as quantum annealing, and present an exhaustive study of the solutions and the estimation of runtimes. Additionally, we analyze the impact of warm-starting QAOA to understand the reasons for the better performance of this approach. We discuss the implications of our results in view of utilizing quantum optimization for industrial applications in the future. In addition to the high demand for better quantum hardware, our results also emphasize the necessity of more and better quantum optimization algorithms, especially for multi-knapsack problems.

Related papers

• Quantum Computing for Discrete Optimization: A Highlight of Three Technologies [0.0]
  This paper focuses on interdisciplinary research between the Operations Research (OR) and Quantum Computing communities. We consider three quantum-powered optimization approaches that make use of different types of quantum hardware available on the market. We illustrate these approaches with experiments on three types of quantum computers: a neutral atom machine from QuEra, a quantum annealer from D-Wave, and a gate-based device from IBM.
  arXiv  Detail & Related papers  (2024-09-02T17:04:47Z)
• Variational Quantum Algorithms for Combinatorial Optimization [0.571097144710995]
  Variational Algorithms (VQA) have emerged as one of the strongest candidates towards reaching practical applicability of NISQ systems. This paper explores the current state and recent developments of VQAs, emphasizing their applicability to Approximate optimization. We implement QAOA circuits with varying depths to solve the MaxCut problem on graphs with 10 and 20 nodes.
  arXiv  Detail & Related papers  (2024-07-08T22:02:39Z)
• 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)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• A Practitioner's Guide to Quantum Algorithms for Optimisation Problems [0.0]
  NP-hard optimisation problems are common in industrial areas such as logistics and finance. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques. It focuses on their near-term potential for noisy intermediate scale quantum devices.
  arXiv  Detail & Related papers  (2023-05-12T08:57:36Z)
• 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)
• Information scrambling and entanglement in quantum approximate optimization algorithm circuits [9.730534141168752]
  Variational quantum algorithms are promising for demonstrating quantum advantages in the noisy intermediate-scale quantum (NISQ) era. We study information scrambling and entanglement in QAOA circuits, respectively, and discover that for a harder problem, more quantum resource is required.
  arXiv  Detail & Related papers  (2023-01-18T11:36:49Z)
• 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)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)
• Optimizing the Optimizer: Decomposition Techniques for Quantum Annealing [0.0]
  Current generation quantum computers are too small to solve real-world problems. In this work, we explore multiple heterogeneous approaches to solving benchmark problems. Our results indicate that solver performance is highly dependent on the structure of the problem graph.
  arXiv  Detail & Related papers  (2020-01-16T21:35:16Z)

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.