Resource allocation remains NP-hard due to combinatorial complexity. While deep reinforcement learning (DRL) methods, such as the Rainbow Deep Q-Network (DQN), improve scalability through prioritized replay and distributional heads, classical function approximators limit their representational power. We introduce Variational Quantum Rainbow DQN (VQR-DQN), which integrates ring-topology variational quantum circuits with Rainbow DQN to leverage quantum superposition and entanglement. We frame the human resource allocation problem (HRAP) as a Markov decision process (MDP) with combinatorial action spaces based on officer capabilities, event schedules, and transition times. On four HRAP benchmarks, VQR-DQN achieves 26.8% normalized makespan reduction versus random baselines and outperforms Double DQN and classical Rainbow DQN by 4.9–13.4%. These gains align with theoretical connections between circuit expressibility, entanglement, and policy quality, demonstrating the potential of quantum-enhanced DRL for large-scale resource allocation. Our implementation is available at: https://github.com/Analytics-Everywhere-Lab/qtrl/.
Resource allocation remains NP-hard due to combinatorial complexity. While deep reinforcement learning (DRL) methods, such as the Rainbow Deep Q-Network (DQN), improve scalability through prioritized replay and distributional heads, classical function approximators limit their representational power. We introduce Variational Quantum Rainbow DQN (VQR-DQN), which integrates ring-topology variational quantum circuits with Rainbow DQN to leverage quantum superposition and entanglement. We frame the human resource allocation problem (HRAP) as a Markov decision process (MDP) with combinatorial action spaces based on officer capabilities, event schedules, and transition times. On four HRAP benchmarks, VQR-DQN achieves 26.8% normalized makespan reduction versus random baselines and outperforms Double DQN and classical Rainbow DQN by 4.9–13.4%. These gains align with theoretical connections between circuit expressibility, entanglement, and policy quality, demonstrating the potential of quantum-enhanced DRL for large-scale resource allocation. Our implementation is available at: https://github.com/Analytics-Everywhere-Lab/qtrl/.
The significant rise in wireless communication, driven by sixth-generation (6G) networks, underscores the urgent need for innovative resource allocation methods to achieve efficiency, scalability, and reliability. This survey explores the integration of Quantum Deep Reinforcement Learning (QDRL) as a strategy to address the challenges posed by dynamic and complex wireless environments. By leveraging quantum computing features such as superposition and entanglement, QDRL enhances computational efficiency and mitigates the scalability limitations of classical and deep reinforcement learning (DRL) in high-dimensional optimization. The study reviews recent QDRL approaches for key resource allocation tasks, including power distribution, user scheduling, and channel assignment, and examines quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNN) for improved decision-making in wireless systems. Furthermore, it emphasizes the connection between quantum computing and DRL, highlighting hybrid architectures that balance computational cost, energy efficiency, and scalability. Finally, the survey identifies research gaps and outlines future directions, paving the way for robust QDRL strategies to support next-generation wireless networks.
In the realm of artificial intelligence, deep rein-forcement learning (RL) agents struggle with generalizability and require substantial computational resources, unlike humans who easily adapt and generalize across tasks. To address these challenges, we introduce Quantum Rainbow, a hybrid algorithm that leverages the neural mechanisms of human decision-making and the efficiency of quantum computing. Quantum Rainbow combines variational quantum circuits with the Rainbow Deep Q-Network (DQN) model to create a novel approach in rein-forcement learning that integrates quantum principles into deep learning paradigms. We evaluate our model using behavioral experiments through the Iowa Gambling Task and 4-Armed Bandit Task. Our investigations reveal a significant relationship between the architecture of quantum circuits and the performance of quantum RL agents. Specifically, using causal discovery methods, we demonstrate the critical role of quantum entanglement in enhancing model performance. These findings not only show promising results but also pave the way for future explorations into optimizing quantum circuit architectures for reinforcement learning applications. This study underscores the potential of quantum-enhanced algorithms to achieve “quantum advantage” by addressing fundamental limitations in conventional deep RL methods.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation
The impending transition to sixth-generation (6G) wireless networks introduces the operational deployment of sub-terahertz and terahertz (sub-THz/THz) frequency bands (0.1 THz to 1.0 THz, specifically the 140 GHz D-Band and 300 GHz transmission windows) to support multi-terabit-per-second (Tbps) data rates and sub-0.1 millisecond Ultra-Reliable Low-Latency Communication (URLLC). However, the physics of THz electromagnetic propagation—characterized by severe atmospheric molecular absorption, extreme free-space path loss, and shadow attenuation from dynamic blockages—demands narrow pencil-beam hybrid beamforming alongside massive Reconfigurable Intelligent Surfaces (RIS). When millions of autonomous vehicles, aerial drones, and mobile devices traverse high-density urban environments, joint real-time spectrum sub-band scheduling, continuous phase-shift tracking, and power allocation induce an NP-hard combinatorial optimization problem scaling exponentially as O(K^N). Classical Deep Reinforcement Learning (DRL) frameworks, such as Deep Q-Networks (DQN) and Soft Actor-Critic (SAC) executing on high-performance host GPUs, suffer from a prohibitive computational inference barrier (requiring 28.5 ms to 74.2 ms per scheduling decision), fundamentally violating the 6G URLLC scheduling budget (<1.0 ms). This paper proposes and rigorously evaluates a Parameterized Variational Quantum Reinforcement Learning (VQRL) framework utilizing quantum state amplitude-phase embedding and parameterized rotation-entanglement quantum circuits (VQC) executed on superconducting NISQ quantum co-processors. By evaluating multi-user allocation policies simultaneously in quantum superposition across a 2^N-dimensional Hilbert space and eliminating multi-cell co-channel interference through inter-tower quantum entanglement, VQRL achieves: • Exponential Latency Reduction: Slashes scheduling execution latency to just 0.078 milliseconds (78 microseconds)—a 365x speedup over GPU-based DQN—comfortably meeting 6G sub-millisecond sub-frame budgets.• Superior Convergence Speed: Achieves a 94.2% reduction in training convergence time (converging within 42 episodes versus 720 episodes for double DQN).• Massive Multi-Terabit Throughput: Delivers an aggregate cell throughput of 1.48 Tbps (a 2.84x improvement over classical DQN baselines) under 3GPP Urban Microcell (UMi) topographies.• High-Mobility Vehicular Beam Tracking: Eliminates 98.6% of vehicular beam-alignment dropouts under 120 km/h mobility through predictive quantum Hamiltonian evolution.• Energy Efficiency: Reduces base station computational energy consumption from 480 Watts to 24 Watts per radio frame. The repository includes the full 31-page primary academic treatise, complete novelty and systems defense dossiers, and full Hindi and Marathi translations.
The power of classical computers is still insufficient for deep reinforcement learning (DRL) problems which has large state space. Thanks to entanglement and superposition, quantum computers have a high computational power. The concept of using this high computing power to solve problems that would be difficult for classical computers is fairly common. In this study, a hybrid approach is proposed to take advantage of the benefits of quantum computers. Deep Q-Network (DQN) algorithm for DRL, optimization operations, and storage operations are performed on the classical computer side of this hybrid approach. In the quantum side, a variational quantum circuit (VQC) is proposed. The proposed method is applied to a navigation problem. The proposed approach is evaluated in terms of target success rate, collision (going out) rate. The proposed approach is compared to DRL solutions in the literature for the navigation problem with classical computers. According to the number of parameters used, the proposed approach appears to be successful. As a result, the performance of the proposed approach has been validated.
With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. However, even the most powerful classical supercomputers find it difficult to execute these tasks. Advancements in quantum computing are leading researchers and tech-giants who strive for better quantum circuits to do machine learning tasks. Current works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. However, it is strenuous to simulate the classical deep learning approach on existing quantum computing devices due to the inflexibility of Deep quantum circuits. Consequently, designing viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices is essential. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Q network-based Reinforcement Learning by remodeling the target network and experience replay into a representation of VQC. In addition, to the reduction in model parameters, quantum information encoding schemes are used to achieve better results than classical neural networks. VQCs are employed for policyselection and decision-making reinforcement learning by approximating the deep Q-value function with target network and experience replay.