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 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.
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.
Quantum networks, leveraging the principles of quantum mechanics, enable unconditionally secure communication and are emerging as a critical component of next-generation infrastructure. However, the scarcity of quantum network resources results in contention among requests for shared links and qubits. Such contention prolongs processing times and degrades resource utilization efficiency, which in turn leads to imbalanced completion times between high- and low-priority requests. This paper addresses the request scheduling problem by proposing DRL-QN, a scheduling framework based on Reinforcement Learning (RL) that optimizes scheduling sequences, reduces the completion time of network requests, and improves fairness. Specifically, to help the agent better perceive request contention and the network environment, request characteristics and network resources are described as the state, enabling accurate scheduling decisions. Furthermore, to ensure a balanced learning objective, the agent’s training is guided by a multi-objective reward function that simultaneously promotes low completion time and fairness. Extensive simulations demonstrate that the proposed DRL-QN method significantly reduces network completion time by 16.22%–21.25% compared with existing schemes, while achieving fairness comparable to the fairness-optimal method and improving by approximately 6.79%–13.61% over other approaches.
Quantum entanglement is the cornerstone of quantum network communication, yet the qubit resources required to establish it are severely limited. Moreover, users within the network impose strict delay constraints on entanglement requests due to various factors, such as the real-time requirements of upper-layer applications or the finite coherence time of userside quantum memories. Consequently, effectively scheduling scarce qubit resources to serve these time-sensitive requests presents a significant challenge. This difficulty is exacerbated by the partial observability of quantum systems and the time-varying nature of qubit generation rates, rendering traditional scheduling algorithms ineffective. Furthermore, while Deep Reinforcement Learning (DRL) offers a promising avenue for quantum network optimization, existing research primarily focuses on network-layer routing, overlooking the finer-grained entanglement scheduling problem subject to strict delay and resource constraints. To bridge this gap, we propose the Deep Quantum Request Scheduler (DQRS), a novel DRL-based algorithm designed to maximize service quality under these dual constraints. Specifically, DQRS integrates a spatio-temporal branching architecture and virtual action slots within an ActorCritic framework. This design enables robust scheduling with continuous actions while adaptively managing fluctuating qubit budgets. Extensive simulation results demonstrate that DQRS achieves up to 44 % performance improvement over traditional methods and a 4 % gain compared to state-of-the-art DRL benchmarks across various resource constraints and observability settings, validating the robustness of the proposed approach.
Deep reinforcement learning (DRL), acting as a novel and powerful paradigm for quantum optimal control, offers transformative opportunities for advancing neutral-atom quantum computing. In this work, we theoretically demonstrate a DRL-based framework for realizing Rydberg controlled-NOT gates that achieve both high speed and high fidelity through the synchronous modulation of multiple pulse parameters without any prior heuristic ansatz. By introducing an incremental-update learning policy, our framework effectively regularizes the exploration of the control landscape, ensuring the generation of smooth, experimentally feasible pulse profiles while significantly reducing computational overhead compared to conventional schemes. Crucially, the framework autonomously discovers an early-cutoff policy by optimally reconciling operation speed with high-precision coherent control. Our optimized protocol achieves a peak average fidelity of 0.9991, significantly outperforming conventional methods and surpassing the critical fault-tolerant threshold. This work establishes a generalizable, AI-driven pathway for designing high-performance quantum gates and provides a robust paradigm for autonomous control field optimization across diverse qubit platforms.
A central challenge in quantum information science and technology is achieving real-time estimation and feedforward control of quantum systems. This challenge is compounded by the inherent inhomogeneity of quantum resources, such as qubit properties and controls, and their intrinsically probabilistic nature. This leads to stochastic challenges in error detection and probabilistic outcomes in processes such as heralded remote entanglement. Given these complexities, optimizing the construction of quantum resource states is an NP-hard problem. In this paper, we address the quantum resource scheduling issue by formulating the problem and simulating it within a digitized environment, allowing the exploration and development of agent-based optimization strategies. We employ reinforcement learning agents within this probabilistic setting and introduce a new framework utilizing a Transformer model that emphasizes self-attention mechanisms for pairs of qubits. This approach facilitates dynamic scheduling by providing real-time, next-step guidance. Our method significantly improves the performance of quantum systems, achieving more than a 3× improvement over rule-based agents, and establishes an innovative framework that improves the joint design of physical and control systems for quantum applications in communication, networking, and computing.
Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum systems in the Noisy Intermediate-Scale Quantum (NISQ) era. Classical agent-based artificial intelligence algorithms provide a framework for the design or control of quantum systems. Traditional reinforcement learning methods are designed for the Markov Decision Process (MDP) and, hence, have difficulty in dealing with partially observable or quantum observable decision processes. Due to the difficulty of building or inferring a model of a specified quantum system, a model-free-based control approach is more practical and feasible than its counterpart of a model-based approach. In this work, we apply a model-free deep recurrent Q-network (DRQN) reinforcement learning method for qubit-based quantum circuit architecture design problems. This paper is the first attempt to solve the quantum circuit design problem from the recurrent reinforcement learning algorithm, while using discrete policy. Simulation results suggest that our long short-term memory (LSTM)-based DRQN method is able to learn quantum circuits for entangled Bell–Greenberger–Horne–Zeilinger (Bell–GHZ) states. However, since we also observe unstable learning curves in experiments, suggesting that the DRQN could be a promising method for AI-based quantum circuit design application, more investigation on the stability issue would be required.