BILP-Q: Quantum Coalition Structure Generation

• URL: http://arxiv.org/abs/2204.13802v1
• Date: Thu, 28 Apr 2022 22:19:50 GMT
• Title: BILP-Q: Quantum Coalition Structure Generation
• Authors: Supreeth Mysore Venkatesh, Antonio Macaluso, Matthias Klusch
• Abstract summary: We propose BILP-Q, the first-ever general quantum approach for solving the Coalition Structure Generation problem (CSGP) We run BILP-Q on medium-size problems using a real quantum annealer (D-Wave)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI. In this work, we propose BILP-Q, the first-ever general quantum approach for solving the Coalition Structure Generation problem (CSGP), which is notably NP-hard. In particular, we reformulate the CSGP in terms of a Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus, we perform a comparative analysis in terms of time complexity between the proposed quantum approach and the most popular classical baselines. Furthermore, we consider standard benchmark distributions for coalition values to test the BILP-Q on small-scale experiments using the IBM Qiskit environment. Finally, since QUBO problems can be solved operating with quantum annealing, we run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).

Related papers

• Bayesian Optimization Priors for Efficient Variational Quantum Algorithms [0.0]
  Quantum computers currently rely on a quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. We propose a hybrid framework for basic computational optimization that reduces the number of shots per time is charged. Using both proposed features, we show that using both proposed features statistically outperforms an implementation within VQAs for simulations.
  arXiv  Detail & Related papers  (2024-06-20T18:00:12Z)
• A quantum annealing approach to the minimum distance problem of quantum codes [0.0]
  We introduce an approach to compute the minimum distance of quantum stabilizer codes by reformulating the problem as a Quadratic Unconstrained Binary Optimization problem. We demonstrate practical viability of our method by comparing the performance of purely classical algorithms with the D-Wave Advantage 4.1 quantum annealer.
  arXiv  Detail & Related papers  (2024-04-26T21:29:42Z)
• 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 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)
• Quantum Annealing Learning Search Implementations [0.0]
  This paper presents the details and testing of two implementations of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer.
  arXiv  Detail & Related papers  (2022-12-21T15:57:16Z)
• NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems [1.1470070927586016]
  We discuss the field of quantum optimization where we solve optimisation problems using quantum computers. We demonstrate this through a proper use case and discuss the current quality of quantum computers. We conclude with discussion on some recent quantum optimization breakthroughs and the current status and future directions.
  arXiv  Detail & Related papers  (2022-12-21T12:56:37Z)
• Quantum Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
  We study the problem of designing worst-case to average-case reductions for quantum algorithms. We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
  arXiv  Detail & Related papers  (2022-12-06T22:01:49Z)
• 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)
• Hybrid Quantum Classical Simulations [0.0]
  We report on two major hybrid applications of quantum computing, namely, the quantum approximate optimisation algorithm (QAOA) and the variational quantum eigensolver (VQE) Both are hybrid quantum classical algorithms as they require incremental communication between a classical central processing unit and a quantum processing unit to solve a problem.
  arXiv  Detail & Related papers  (2022-10-06T10:49:15Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Q-Match: Iterative Shape Matching via Quantum Annealing [64.74942589569596]
  Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) This paper proposes Q-Match, a new iterative quantum method for QAPs inspired by the alpha-expansion algorithm. Q-Match can be applied for shape matching problems iteratively, on a subset of well-chosen correspondences, allowing us to scale to real-world problems.
  arXiv  Detail & Related papers  (2021-05-06T17:59:38Z)

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.