The purpose of this research is to show the capabilities that quantum computing has to offer for AI. Therefore, the scheduling problem was chosen in order to demonstrate and compare the classical reinforcement learning (CRL) agent with the quantum computing reinforcement learning (QC-RL) agent. This constrained combinatorial optimization problem involves scheduling an oil field with several drilling rigs which are limited in capacity, area limitations, mandatory maintenance periods, weather restrictions and a mobilization costs which is considered along with well priorities and deadlines. Deep reinforcement learning (DRL) can learn adaptive scheduling policies, but value-based agents such as Deep Q-Networks (DQN) are known to exhibit high variance across random initializations. This paper investigates whether a hybrid quantum-classical value function can improve this reliability. We formulate realistic multi-rig drilling scheduling as a Markov Decision Process with action masking over an eight-constraint simulator (24 wells, 6 rigs, 3 areas, 80-day horizon) and compare a classical DQN against a hybrid Quantum DQN in which the value network’s core is a variational quantum circuit via a 4-qubit ZFeatureMap encoding followed by a RealAmplitudes ansatz with 12 trainable quantum parameters, executed using a PyTorch-Qiskit interface. Both agents share an identical training and evaluation protocol (3 seeds × 30 episodes, greedy evaluation) to ensure a controlled comparison. Under greedy evaluation, the hybrid Quantum DQN completes 22.3/24 wells on average versus 17/24 for the classical DQN, achieves an 12.87% higher mean reward (9.12M vs 8.08M), and most significantly reduces the across-seed reward standard deviation by roughly 6.8× (0.202M vs 1.38M). These results indicate that the structurally constrained quantum value function acts as an implicit regularizer, yielding scheduling policies that are markedly more consistent across initializations than a substantially larger classical network. This simulated quantum computing experiment demonstrates the potential for quantum computing in the field of AI.
The purpose of this research is to show the capabilities that quantum computing has to offer for AI. Therefore, the scheduling problem was chosen in order to demonstrate and compare the classical reinforcement learning (CRL) agent with the quantum computing reinforcement learning (QC-RL) agent. This constrained combinatorial optimization problem involves scheduling an oil field with several drilling rigs which are limited in capacity, area limitations, mandatory maintenance periods, weather restrictions and a mobilization costs which is considered along with well priorities and deadlines. Deep reinforcement learning (DRL) can learn adaptive scheduling policies, but value-based agents such as Deep Q-Networks (DQN) are known to exhibit high variance across random initializations. This paper investigates whether a hybrid quantum-classical value function can improve this reliability. We formulate realistic multi-rig drilling scheduling as a Markov Decision Process with action masking over an eight-constraint simulator (24 wells, 6 rigs, 3 areas, 80-day horizon) and compare a classical DQN against a hybrid Quantum DQN in which the value network’s core is a variational quantum circuit via a 4-qubit ZFeatureMap encoding followed by a RealAmplitudes ansatz with 12 trainable quantum parameters, executed using a PyTorch-Qiskit interface. Both agents share an identical training and evaluation protocol (3 seeds × 30 episodes, greedy evaluation) to ensure a controlled comparison. Under greedy evaluation, the hybrid Quantum DQN completes 22.3/24 wells on average versus 17/24 for the classical DQN, achieves an 12.87% higher mean reward (9.12M vs 8.08M), and most significantly reduces the across-seed reward standard deviation by roughly 6.8× (0.202M vs 1.38M). These results indicate that the structurally constrained quantum value function acts as an implicit regularizer, yielding scheduling policies that are markedly more consistent across initializations than a substantially larger classical network. This simulated quantum computing experiment demonstrates the potential for quantum computing in the field of AI.
ABSTRACT Quantum computing has entered the noisy intermediate‐scale quantum (NISQ) era, where limited qubit numbers, short coherence times, and high error rates pose significant challenges to reliable large‐scale execution. Efficient scheduling and resource allocation across heterogeneous quantum hardware are therefore crucial for maximizing system throughput, fidelity, and fairness. In this work, we propose a hardware‐aware reinforcement learning framework for quantum task scheduling in distributed NISQ systems. Our design explicitly models qubit‐level variability, including connectivity degree, coherence times, error rates, and throughput, while integrating task‐level constraints such as deadlines, priorities, and concurrency requirements. We formulate the scheduling problem as a constrained optimization task and instantiate it as a Markov decision process (MDP), enabling reinforcement learning agents to learn adaptive strategies. Specifically, we implement deep Q‐network (DQN) and proximal policy optimization (PPO) agents, and compare them against heuristic and random baselines. Experimental results demonstrate that PPO consistently outperforms DQN and heuristic methods, achieving higher task completion rates, fewer deadline violations, and more robust adaptation across different reward configurations. This work bridges quantum hardware modeling with reinforcement learning‐based scheduling, providing a practical pathway for resource optimization in distributed quantum computing environments.
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.
In recent years, quantum computing (QC) has been getting a lot of attention from industry and academia. Especially, among various QC research topics, variational quantum circuit (VQC) enables quantum deep reinforcement learning (QRL). Many studies of QRL have shown that the QRL is superior to the classical reinforcement learning (RL) methods under the constraints of the number of training parameters. This paper extends and demonstrates the QRL to quantum multi-agent RL (QMARL). However, the extension of QRL to QMARL is not straightforward due to the challenge of the noise intermediate-scale quantum (NISQ) and the non-stationary properties in classical multi-agent RL (MARL). Therefore, this paper proposes the centralized training and decentralized execution (CTDE) QMARL framework by designing novel VQCs for the framework to cope with these issues. To corroborate the QMARL framework, this paper conducts the QMARL demonstration in a single-hop environment where edge agents offload packets to clouds. The extensive demonstration shows that the proposed QMARL framework enhances 57.7% of total reward than classical frameworks.
We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train parameterized quantum circuits (PQCs) with two different encoding strategies in a hybrid quantum-classical setup as well as a classical neural network baseline with the double deep Q network (DDQN) reinforcement learning algorithm. Quantum deep reinforcement learning (QDRL) has previously been studied in several relatively simple benchmark environments, mainly from the OpenAI gym suite. However, scaling behavior and applicability of QDRL to more demanding tasks closer to real-world problems e.g., from the robotics domain, have not been studied previously. Here, we show that quantum circuits in hybrid quantum-classic reinforcement learning setups are capable of learning optimal policies in multiple robotic navigation scenarios with notably fewer trainable parameters compared to a classical baseline. Across a large number of experimental configurations, we find that the employed quantum circuits outperform the classical neural network baselines when equating for the number of trainable parameters. Yet, the classical neural network consistently showed better results concerning training times and stability, with at least one order of magnitude of trainable parameters more than the best-performing quantum circuits. However, validating the robustness of the learning methods in a large and dynamic environment, we find that the classical baseline produces more stable and better performing policies overall. For the two encoding schemes, we observed better results for consecutively encoding the classical state vector on each qubit compared to encoding each component on a separate qubit. Our findings demonstrate that current hybrid quantum machine-learning approaches can be scaled to simple robotic problems while yielding sufficient results, at least in an idealized simulated setting, but there are yet open questions regarding the application to considerably more demanding tasks. We anticipate that our work will contribute to introducing quantum machine learning in general and quantum deep reinforcement learning in particular to more demanding problem domains and emphasize the importance of encoding techniques for classic data in hybrid quantum-classical settings.
Quantum computing is a disruptive computing paradigm with potential applications in cybersecurity, artificial intelligence, and drug discovery. However, the experimental performance of quantum algorithm is severely limited by the stability and control precision of the quantum chip. This work aims to develop an efficient scheduling framework for maintaining parameters of quantum chip and control pulse automatically. We introduce a novel reinforcement learning framework called QRLSchedule formulating the scheduling for maintenance of parameters as a learning problem and propose the Machine-First-Operation-Second (MFOS) method to reduce the agent’s inference time in Reinforcement Learning. The results of numerical simulations show an average improvement of 86.74% in makespan over Constrained Programming (CP) based scheduling method, demonstrating the effectiveness of the method.
Quantum computing promises significant computational advantages over classical methods in certain applications but is currently too unstable for executing complex tasks. High-fidelity quantum circuits are critical to overcoming this challenge, yet they are computationally challenging to design. On current noisy intermediate-scale quantum (NISQ) hardware, shallower circuits with fewer two-qubit gates generally run with higher fidelity. Increased fidelity can be sought by sequentially rewriting the circuit, which involves a vast search space. This costly search space exploration could be circumvented by using reinforcement learning (RL) to teach an agent to rewrite the circuit in a sequential decision-making process. This thesis aims to implement a Deep Q-Network (DQN) algorithm to optimize BernsteinVazirani (BVZ) circuits. The DQN algorithm interacts with a Cirq-based environment. The environment provides valid rewrite actions to the DQN algorithm, which gains a scalar reward emphasizing depth and gate reduction. In the thesis we compare two different state encodings as input for the neural network: a compact state summarizing vector representation with three features, and an image-like encoding that maps circuit state topology with tokens through a fixed embedding. Both input encodings use the same action rewrite set and reward design. The network is implemented in PyTorch and trained with Adam optimizer, and runs on GPU via CUDA. Empirically, the agent shows promising initial optimizing performance. On several BVZ circuits with varying qubit count and topology, the agent drives the depth toward the theoretical optimum of three. The simplistic vector encoding already reaches reasonable circuit depth plateaus, with the image-like encoding together with hyperparameter tuning improving reliability, showing avenues for continued improvement. We conclude that DQN is a viable foundation for automated circuit optimization on BVZ circuits. To achieve reliable circuit optimization at scale, future research should investigate even richer encoding methods and reward function shaping. Finally, the results support applying the DQN to be applied to circuit families beyond BVZ.