Constrained-optimization Approach Delivers Superior Classical Performance for Graph Partitioning via Quantum-ready Method

• URL: http://arxiv.org/abs/2006.15067v1
• Date: Fri, 26 Jun 2020 15:55:12 GMT
• Title: Constrained-optimization Approach Delivers Superior Classical Performance for Graph Partitioning via Quantum-ready Method
• Abstract summary: Graph partitioning is an important set of compute-intense (NP-hard) graph problems. We sampled solutions to the problem via two different quantum-ready methods. We find that both approaches often deliver better partitions than the purpose-built classical graph partitioners.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph partitioning is one of an important set of well-known compute-intense (NP-hard) graph problems that devolve to discrete constrained optimization. We sampled solutions to the problem via two different quantum-ready methods to understand the pros and cons of each method. First we formulated and sampled the problem as a quadratic unconstrained binary optimization (QUBO) problem, via the best known QUBO formulation, using a best-in-class QUBO sampler running purely classically. Second, we formulated the problem at a higher level, as a set of constraints and an objective function, and sampled it with a constrained-optimization sampler (which internally samples the problem via QUBOs also sampled classically). We find that both approaches often deliver better partitions than the purpose-built classical graph partitioners. Further, we find that the constrained-optimization approach is often able to deliver better partitions in less time than the bespoke-QUBO approach, without knowledge of the graph-partitioning problem. Stepping back from graph partitioning itself, one key controversial question is whether bespoke algorithms or general tools are more likely to deliver the power of QCs to real-world users. These results bear on that question, though they require confirmation on other problems and instances as well as replacement of the low-level sampler by a QC. Still, this early evidence supports the proposition that general tools will contribute significantly to a range of problems, expanding the impact of QCs. This benefit is independent of the low-level sampler employed, whether software or QC, so reinforces the need for more work on high-level optimization. An early version of such software is commercially available in the cloud today, delivering superior classical performance for some problems, enables quantum-forward organizations to migrate to quantum-ready methods now.

Related papers

• Quantum Optimization Methods for the Generalized Traveling Salesman Problem [2.4029307306254513]
  This paper studies quantum optimization baselines for the Generalized Traveling Salesman Problem (GTSP)<n>We propose a novel GTSP QUBO formulation focused on maintaining feasible solutions for quantum annealing.<n>We implement a constrained QAOA variant using an XY-mixer, which preserves the stepwise Hamming weight in the ideal circuit model.
  arXiv  Detail & Related papers  (2026-04-28T11:58:35Z)
• Adaptive Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems [4.266376725904727]
  We propose a hybrid classical--quantum framework based on graph shrinking to reduce the number of variables and constraints in QUBO formulations of optimization problems.<n>Our approach improves solution feasibility, reduces repair complexity, and enhances quantum optimization quality on hardware-limited instances.
  arXiv  Detail & Related papers  (2025-06-17T07:11:48Z)
• Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection [3.6021182997326022]
  We formulate a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for solving using quantum computationcolorblue. The formulation aims to find communities with maximal self-sufficiency and minimal power flowing between them. We benchmark the solution quality for multiple approaches: D-Wave's hybrid quantum-classical solvers, classicals, and a branch-and-bound solver.
  arXiv  Detail & Related papers  (2024-07-09T11:44:58Z)
• Decision-focused Graph Neural Networks for Combinatorial Optimization [62.34623670845006]
  An emerging strategy to tackle optimization problems involves the adoption of graph neural networks (GNNs) as an alternative to traditional algorithms. Despite the growing popularity of GNNs and traditional algorithm solvers in the realm of CO, there is limited research on their integrated use and the correlation between them within an end-to-end framework. We introduce a decision-focused framework that utilizes GNNs to address CO problems with auxiliary support.
  arXiv  Detail & Related papers  (2024-06-05T22:52:27Z)
• A Hybrid Quantum-Classical Approach to the Electric Mobility Problem [0.8796261172196743]
  We suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. We benchmark the performance of the decomposition technique with classical and quantum-inspired metaheuristics. The major advantage of the proposed approach is that it enables quantum-based methods for this realistic problem with many inequality constraints.
  arXiv  Detail & Related papers  (2023-10-04T12:14:56Z)
• 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)
• QuAnt: Quantum Annealing with Learnt Couplings [18.40480332882025]
  We learn QUBO forms from data through gradient backpropagation instead of deriving them. We demonstrate the advantages of learnt QUBOs on the diverse problem types of graph matching, 2D point cloud alignment and 3D rotation estimation.
  arXiv  Detail & Related papers  (2022-10-13T17:59:46Z)
• Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem [0.8434687648198277]
  The Bin Packing Problem (BPP) stands out as a paradigmatic optimization problem in logistics. We have recently proposed a hybrid approach to the one dimensional BPP. We compare the performance of our procedure with other classical approaches.
  arXiv  Detail & Related papers  (2022-07-15T13:09:12Z)
• Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization [44.96576908957141]
  We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linear iterations problems on quantum computers.
  arXiv  Detail & Related papers  (2022-03-23T18:00:03Z)
• 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)
• Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process [56.55075925645864]
  The problem of constrained decision process (CMDP) is investigated, where an agent aims to maximize the expected accumulated discounted reward subject to multiple constraints. A new utilities-dual convex approach is proposed with novel integration of three ingredients: regularized policy, dual regularizer, and Nesterov's gradient descent dual. This is the first demonstration that nonconcave CMDP problems can attain the lower bound of $mathcal O (1/epsilon)$ for all complexity optimization subject to convex constraints.
  arXiv  Detail & Related papers  (2021-10-20T02:57:21Z)
• Quadratic Unconstrained Binary Optimisation via Quantum-Inspired Annealing [58.720142291102135]
  We present a classical algorithm to find approximate solutions to instances of quadratic unconstrained binary optimisation. We benchmark our approach for large scale problem instances with tuneable hardness and planted solutions.
  arXiv  Detail & Related papers  (2021-08-18T09:26:17Z)
• Accelerating variational quantum algorithms with multiple quantum processors [78.36566711543476]
  Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages. Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data. Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
  arXiv  Detail & Related papers  (2021-06-24T08:18:42Z)
• A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs [91.07247251502564]
  We propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity.
  arXiv  Detail & Related papers  (2021-06-09T09:18:18Z)

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.