Tensor Network Based HOBO Solver

• URL: http://arxiv.org/abs/2407.16106v1
• Date: Tue, 23 Jul 2024 00:33:34 GMT
• Title: Tensor Network Based HOBO Solver
• Authors: Yuichiro Minato,
• Abstract summary: The proposed solver is a promising tool with significant potential for future extensions in terms of formulation. This solver holds promising potential for a wide range of applications in quantum computing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of quantum computing, combinatorial optimization problems are typically addressed using QUBO (Quadratic Unconstrained Binary Optimization) solvers. However, these solvers are often insufficient for tackling higher-order problems. In this paper, we introduce a novel and efficient solver designed specifically for HOBO (Higher-Order Binary Optimization) problem settings. Our approach leverages advanced techniques to effectively manage the complexity and computational demands associated with high-dimensional optimization tasks. The proposed solver is a promising tool with significant potential for future extensions in terms of formulation. This solver holds promising potential for a wide range of applications in quantum computing.

Related papers

• A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem [2.9730678241643815]
  We propose a predictive solver selection approach based on supervised machine learning. In more than 70% of the cases, the best solver is selected, and in about 90% of the problems, a solver in the top two is selected. This exploration proves the potential of machine learning in quantum solver selection and lays the foundations for its automation.
  arXiv  Detail & Related papers  (2024-08-07T08:14:58Z)
• Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers [2.9730678241643815]
  A framework is proposed to automatically convert conventional optimization problems into quantum solvers. The framework offers instruments for analyzing solution validity and quality. It represents a significant advancement towards automating quantum computing solutions.
  arXiv  Detail & Related papers  (2024-06-18T17:56:10Z)
• Quantum-based Distributed Algorithms for Edge Node Placement and Workload Allocation [8.937905773981702]
  We present a mixed-integer linear programming (MILP) model for optimal edge server placement and workload allocation. Existing quantum solvers are limited to solving unconstrained binary programming problems. Our numerical experiments demonstrate the practicality of leveraging quantum supremacy to solve complex optimization problems in edge computing.
  arXiv  Detail & Related papers  (2023-06-01T21:33:08Z)
• Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA [4.670374869377859]
  A core step in solving optimization problems with quantum algorithms is the problem formulation. Recent studies show that many problems can be solved more efficiently in their native Polynomial Unconstrained Optimization forms. Our evaluation on suitable benchmark functions, shows that PUBO formulations generally yield better results, while requiring less qubits.
  arXiv  Detail & Related papers  (2023-05-05T09:37:48Z)
• Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization [72.41480594026815]
  Constrained optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
  arXiv  Detail & Related papers  (2022-11-16T18:59:54Z)
• QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines [81.4597482536073]
  Quantum approximate optimization algorithms (QAOAs) utilize the power of quantum machines and inherit the spirit of adiabatic evolution. We propose QAOA-in-QAOA ($textQAOA2$) to solve arbitrary large-scale MaxCut problems using quantum machines. Our method can be seamlessly embedded into other advanced strategies to enhance the capability of QAOAs in large-scale optimization problems.
  arXiv  Detail & Related papers  (2022-05-24T03:49:10Z)
• Classically-Boosted Quantum Optimization Algorithm [0.0]
  We explore a natural approach to leveraging existing classical techniques to enhance quantum optimization. Specifically, we run a classical algorithm to find an approximate solution and then use a quantum circuit to search its "neighborhood" for higher-quality solutions. We demonstrate the applications of CBQOA to Max 3SAT and Max Bisection, and provide empirical evidence that it outperforms previous approaches on these problems.
  arXiv  Detail & Related papers  (2022-03-25T23:36:14Z)
• 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)
• Polynomial unconstrained binary optimisation inspired by optical simulation [52.11703556419582]
  We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization. We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
  arXiv  Detail & Related papers  (2021-06-24T16:39:31Z)
• 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)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

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.