A Quantum-Classical Hybrid Branch & Bound Algorithm
- URL: http://arxiv.org/abs/2511.19501v1
- Date: Sun, 23 Nov 2025 17:44:03 GMT
- Title: A Quantum-Classical Hybrid Branch & Bound Algorithm
- Authors: András Czégel, Dávid Sipos, Boglárka G. -Tóth,
- Abstract summary: We propose a complete quantum-classical hybrid branch-and-bound algorithm (QCBB) to solve binary linear programs with equality constraints.<n>We show numerical results on set partitioning problem instances and provide many details about the characteristics of the different steps of the algorithm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We propose a complete quantum-classical hybrid branch-and-bound algorithm (QCBB) to solve binary linear programs with equality constraints. That includes bound calculation, convergence metrics and optimality guarantee to the quantum optimization based algorithm, which makes our method directly comparable to classical methods. Key aspects of the proposed algorithm are (i) encapsulation of the quantum optimization method, (ii) utilization of noisy samples for problem reduction, (iii) classical approximation based bound calculation, (iv) branch and bound traits like gap-based stopping criterion and monotonic increase in solution quality, (v) integrated composition of many different solutions that can be improved individually. We show numerical results on set partitioning problem instances and provide many details about the characteristics of the different steps of the algorithm.
Related papers
- A Simplification Method for Inequality Constraints in Integer Binary Encoding HOBO Formulations [0.0]
The proposed method addresses challenges associated with Quadratic Unconstrained Binary Optimization (QUBO) formulations.<n>By efficiently integrating constraints, the method enhances the computational efficiency and accuracy of both quantum and classical solvers.
arXiv Detail & Related papers (2025-01-16T17:06:25Z) - A Quantum Genetic Algorithm Framework for the MaxCut Problem [49.59986385400411]
The proposed method introduces a Quantum Genetic Algorithm (QGA) using a Grover-based evolutionary framework and divide-and-conquer principles.<n>On complete graphs, the proposed method consistently achieves the true optimal MaxCut values, outperforming the Semidefinite Programming (SDP) approach.<n>On ErdHos-R'enyi random graphs, the QGA demonstrates competitive performance, achieving median solutions within 92-96% of the SDP results.
arXiv Detail & Related papers (2025-01-02T05:06:16Z) - Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches [0.0]
This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems.
Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver.
Our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies.
arXiv Detail & Related papers (2024-10-30T08:41:29Z) - Analysis of the Non-variational Quantum Walk-based Optimisation Algorithm [0.0]
This paper introduces in detail a non-variational quantum algorithm designed to solve a wide range of optimisation problems.
The algorithm returns optimal and near-optimal solutions from repeated preparation and measurement of an amplified state.
arXiv Detail & Related papers (2024-07-29T13:54:28Z) - Lagrangian Duality in Quantum Optimization: Overcoming QUBO Limitations for Constrained Problems [0.0]
We propose an approach to solving constrained optimization problems based on embedding the concept of Lagrangian duality into the framework of adiabatic quantum computation.
Within the setting of circuit-model fault-tolerant quantum computation, we demonstrate that this approach achieves a quadratic improvement in circuit depth and maintains a constraint-independent circuit width.
arXiv Detail & Related papers (2023-10-06T19:09:55Z) - Linearization Algorithms for Fully Composite Optimization [61.20539085730636]
This paper studies first-order algorithms for solving fully composite optimization problems convex compact sets.
We leverage the structure of the objective by handling differentiable and non-differentiable separately, linearizing only the smooth parts.
arXiv Detail & Related papers (2023-02-24T18:41:48Z) - A Comparative Study On Solving Optimization Problems With Exponentially
Fewer Qubits [0.0]
We evaluate and improve an algorithm based on Variational Quantum Eigensolver (VQE)
We highlight the numerical instabilities generated by encoding the problem into the variational ansatz.
We propose a classical optimization procedure to find the ground-state of the ansatz in less iterations with a better objective.
arXiv Detail & Related papers (2022-10-21T08:54:12Z) - First-Order Algorithms for Nonlinear Generalized Nash Equilibrium
Problems [88.58409977434269]
We consider the problem of computing an equilibrium in a class of nonlinear generalized Nash equilibrium problems (NGNEPs)
Our contribution is to provide two simple first-order algorithmic frameworks based on the quadratic penalty method and the augmented Lagrangian method.
We provide nonasymptotic theoretical guarantees for these algorithms.
arXiv Detail & Related papers (2022-04-07T00:11:05Z) - Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings.
We exploit a warm-start strategy to amortize the estimation of the exact gradient.
By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
arXiv Detail & Related papers (2021-11-29T15:10:09Z) - Conditional gradient methods for stochastically constrained convex
minimization [54.53786593679331]
We propose two novel conditional gradient-based methods for solving structured convex optimization problems.
The most important feature of our framework is that only a subset of the constraints is processed at each iteration.
Our algorithms rely on variance reduction and smoothing used in conjunction with conditional gradient steps, and are accompanied by rigorous convergence guarantees.
arXiv Detail & Related papers (2020-07-07T21:26:35Z) - 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) - Quantum approximate algorithm for NP optimization problems with
constraints [12.294570891467604]
In this paper, we formalize different constraint types to equalities, linear inequalities, and arbitrary form.
Based on this, we propose constraint-encoding schemes well-fitting into the QAOA framework for solving NP optimization problems.
The implemented algorithms demonstrate the effectiveness and efficiency of the proposed scheme.
arXiv Detail & Related papers (2020-02-01T04:45:41Z)
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.