# Quantum Reinforcement Learning for Cost and Delay Tradeoffs in Quantum Cloud Orchestration

arXiv: https://arxiv.org/abs/2609.27446 · HTML: https://mlsanity.com/paper/2609.27446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 13 of 13)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Parametrized quantum policies for reinforcement learning](https://mlsanity.com/paper/2103.05577/) (2021)
- [Quantum Cloud Computing: A Review, Open Problems, and Future Directions](https://mlsanity.com/paper/2404.11420/) (2024)
- [Simultaneous execution of quantum circuits on current and near-future NISQ systems](https://mlsanity.com/paper/2112.07091/) (2021)
- [QFaaS: A Serverless Function-as-a-Service Framework for Quantum Computing](https://mlsanity.com/paper/2205.14845/) (2022)
- [Adaptive job and resource management for the growing quantum cloud](https://mlsanity.com/paper/2203.13260/) (2022)
- [Stochastic Qubit Resource Allocation for Quantum Cloud Computing](https://mlsanity.com/paper/2210.12343/) (2022)
- [DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing](https://mlsanity.com/paper/2407.02748/) (2024)
- [Qonductor: A Cloud Orchestrator for Quantum Computing](https://mlsanity.com/paper/2408.04312/) (2024)
- [Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning](https://mlsanity.com/paper/2506.10889/) (2025)

## Abstract (arXiv)

Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressing the tradeoff between execution costs and system performance. While heuristic methods rely on predefined scheduling rules, classical deep reinforcement learning (DRL) models may require more trainable parameters in this setting. Motivated by the potential of parameterised quantum circuits (PQCs) as compact function approximators, we propose QRLQ, a cost-delay-aware quantum cloud scheduling framework integrating PQCs with a dueling double deep Q-network (D3QN) to dynamically account for both cost and delay. Our simulation results show that QRLQ achieves lower mean cost and delay than the heuristic baselines, achieving a 5-11% lower mean cost relative to availability-based and rotation-based heuristics and reducing mean delay by 17% and 82% relative to the strongest and weakest heuristic baselines, respectively, while retaining execution fidelity within 2% of a fidelity-greedy policy. Compared with the classical DRL baseline, QRLQ achieves comparable scheduling performance while using 72% fewer trainable parameters. This work explores the feasibility of using QRL for task orchestration in quantum cloud environments and demonstrates its potential for cost-delay-aware quantum resource management.