mlsanityData to 14 Oct 2026

Quantum-Efficient Reinforcement Learning Solutions for Last-Mile On-Demand Delivery

arXiv 2508.09183 ↗First posted 7 Aug 2025quant-ph cs.AI cs.LGcited by 1

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

Quantum computation has demonstrated a promising alternative to solving the NP-hard combinatorial problems. Specifically, when it comes to optimization, classical approaches become intractable to account for large-scale solutions. Specifically, we investigate quantum computing to solve the large-scale Capacitated Pickup and Delivery Problem with Time Windows (CPDPTW). In this regard, a Reinforcement Learning (RL) framework augmented with a Parametrized Quantum Circuit (PQC) is designed to minimize the travel time in a realistic last-mile on-demand delivery. A novel problem-specific encoding quantum circuit with an entangling and variational layer is proposed. Moreover, Proximal Policy Optimization (PPO) and Quantum Singular Value Transformation (QSVT) are designed for comparison through numerical experiments, highlighting the superiority of the proposed method in terms of the scale of the solution and training complexity while incorporating the real-world constraints.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 17 references that are in the map. First posted on arXiv 7 Aug 2025.

Builds on

4 of 17 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search7 Oct
  2. 2024An Introduction to Quantum Reinforcement Learning (QRL)9 Sep

Other references · 2

  1. 2022Equivariant quantum circuits for learning on weighted graphscited by 41
  2. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026APGEM: Adaptive Policy-Guided Error Mitigation for Quantum Reinforcement Learning on a Real-World CVRP Case Studycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Quantum Reinforcement Learning with Transformers for the Capacitated Vehicle Routing Problem
  2. 2026Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
  3. 2025Understanding the Nature of Depth-1 Equivariant Quantum Circuit
  4. 2025Dissecting Quantum Reinforcement Learning: A Systematic Evaluation of Key Components
  5. 2026Reinforcement Learning for Quantum Technology
  6. 2025Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning
  7. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
  8. 2025Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning