Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization

• URL: http://arxiv.org/abs/2605.01574v1
• Date: Sat, 02 May 2026 18:55:48 GMT
• Title: Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
• Abstract summary: We present a new hybrid approach that combines the Quantum Approximate Algorithm Optimization (QAOA) into the Quantum Reinforcement Learning (QRL) policy network.<n>This enhancement enables the agent to exploit problem specific particular quantum correlations when learning policies, and so richer exploration of the routing solution space.
• Score: 0.5131152350448099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle Routing Problem (VRP) is one of the most complex NP-hard combinatorial optimization problem in transportation and logistics that requires a dynamic solution approach. In this paper we present a new hybrid approach that combines the Quantum Approximate Optimization Algorithm (QAOA) into the QRL policy network, instead of the usual variational layers, QAOA mixing and cost Hamiltonian layers. This enhancement enables the agent to exploit problem specific particular quantum correlations when learning policies, and so richer exploration of the routing solution space. The QAOA-augmented QRL framework shows quicker convergence in training and can tackle larger VRP instances that are beyond the reach of Grover's Adaptive Search (GAS) and Quantum Reinforcement Learning (QRL) approaches. Experiments on standard VRP instances demonstrate better solutions, fewer episodes to converge and good memory usage on near term quantum hardware simulators. These findings demonstrate QAOA- integrated QRL as a viable approach to scalable, high quality quantum-assisted combinatorial optimization.

Related papers

• Hardware-Efficient Quantum Optimization for Transportation Networks via Compressed Adiabatic Evolution [1.409283414986451]
  Transportation systems such as urban logistics, vehicle routing, and infrastructure planning require solving large-scale optimization problems under complex constraints.<n>We develop a hardware-grounded hybrid quantum optimization framework that uses Approximate Quantum Compilation (AQC) to compress early segments of digitized adiabatic evolution into shallow circuits.<n>Results show that moderate prefix compression reduces two-qubit gate depth while maintaining or improving feasible solution, particularly for routing problems.
  arXiv  Detail & Related papers  (2026-04-28T23:45:54Z)
• MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments [25.155719953156723]
  Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases.<n>We propose a distributed framework for QRL where multiple agents learn independently, distributing the load of joint training from individual machines.<n>Our method works well for environments with disjoint sets of action and observation spaces, but can also be extended to other systems with reasonable approximations.
  arXiv  Detail & Related papers  (2026-04-13T07:44:23Z)
• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability [0.0]
  The Traveling Salesman Problem (TSP) is a fundamental challenge in optimization, widely applied in logistics and transportation.<n>Traditional algorithms often struggle to produce high-quality solutions within reasonable timeframes.<n>This study investigates the potential of the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical method.
  arXiv  Detail & Related papers  (2025-12-11T17:00:24Z)
• Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem [3.652509571098291]
  The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem in logistics.<n>We propose a hybrid optimization approach that integrates deep reinforcement learning (RL) to automate the selection of penalty parameter values within both classical (RL-C-ALM) and quantum-enhanced (RL-Q-ALM) ALM solvers.
  arXiv  Detail & Related papers  (2025-09-18T08:38:29Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Solving Large-Scale QUBO with Transferred Parameters from Multilevel QAOA of low depth [2.1215729831364887]
  We propose a fast hybrid multilevel algorithm with QAOA parameterization throughout the multilevel hierarchy and its reinforcement with genetic algorithms.<n>Results highlight the practical potential of multilevel QAOA as a scalable method for optimization on near-term quantum devices.
  arXiv  Detail & Related papers  (2025-05-16T17:18:36Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Reinforcement Learning Quantum Local Search [0.0]
  We propose a reinforcement learning (RL) based approach to train an agent for improved subproblem selection in Quantum Local Search (QLS) Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems.
  arXiv  Detail & Related papers  (2023-04-13T13:07:19Z)
• A Copositive Framework for Analysis of Hybrid Ising-Classical Algorithms [18.075115172621096]
  We present a formal analysis of hybrid algorithms in the context of solving mixed-binary quadratic programs via Ising solvers. We propose to solve this reformulation with a hybrid quantum-classical cutting-plane algorithm.
  arXiv  Detail & Related papers  (2022-07-27T16:47:32Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Accelerating Quadratic Optimization with Reinforcement Learning [39.64039435793601]
  We show how Reinforcement Learning can learn a policy to tune parameters to accelerate convergence. Our policy, RLQP, significantly outperforms state-of-the-art QP solvers by up to 3x. RLQP generalizes surprisingly well to previously unseen problems with varying dimension and structure from different applications.
  arXiv  Detail & Related papers  (2021-07-22T17:59:10Z)
• Sample-Efficient Automated Deep Reinforcement Learning [33.53903358611521]
  We propose a population-based automated RL framework to meta-optimize arbitrary off-policy RL algorithms. By sharing the collected experience across the population, we substantially increase the sample efficiency of the meta-optimization. We demonstrate the capabilities of our sample-efficient AutoRL approach in a case study with the popular TD3 algorithm in the MuJoCo benchmark suite.
  arXiv  Detail & Related papers  (2020-09-03T10:04:06Z)
• 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.