Journal articles: 'Quantum Reinforcement Learning'

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Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn. "Quantum Reinforcement Learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (2008): 1207–20. http://dx.doi.org/10.1109/tsmcb.2008.925743.

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Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (2021): 33. http://dx.doi.org/10.3390/photonics8020033.

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Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

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Martín-Guerrero, José D., and Lucas Lamata. "Reinforcement Learning and Physics." Applied Sciences 11, no. 18 (2021): 8589. http://dx.doi.org/10.3390/app11188589.

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Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations.

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Lockwood, Owen, and Mei Si. "Reinforcement Learning with Quantum Variational Circuit." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (2020): 245–51. http://dx.doi.org/10.1609/aiide.v16i1.7437.

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The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.

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Chen, Samuel Yen-Chi, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, and Ying-Jer Kao. "Variational quantum reinforcement learning via evolutionary optimization." Machine Learning: Science and Technology 3, no. 1 (2022): 015025. http://dx.doi.org/10.1088/2632-2153/ac4559.

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Abstract Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL in the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

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Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs are trained more effectively than restricted Boltzmann machines (RBM) with the same number of nodes. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This method also outperforms the reinforcement learning method that uses RBMs.

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Yun, Won Joon, Jihong Park, and Joongheon Kim. "Quantum Multi-Agent Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11087–95. http://dx.doi.org/10.1609/aaai.v37i9.26313.

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Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. Exploiting this dyadic trainability as meta-learning capability, we propose quantum meta MARL (QM2ARL) that first applies angle training for meta-QNN learning, followed by pole training for few-shot or local-QNN training. To avoid overfitting, we develop an angle-to-pole regularization technique injecting noise into the pole domain during angle training. Furthermore, by exploiting the pole as the memory address of each trained QNN, we introduce the concept of pole memory allowing one to save and load trained QNNs using only two-parameter pole values. We theoretically prove the convergence of angle training under the angle-to-pole regularization, and by simulation corroborate the effectiveness of QM2ARL in achieving high reward and fast convergence, as well as of the pole memory in fast adaptation to a time-varying environment.

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Wu, Shaojun, Shan Jin, Dingding Wen, Donghong Han, and Xiaoting Wang. "Quantum reinforcement learning in continuous action space." Quantum 9 (March 12, 2025): 1660. https://doi.org/10.22331/q-2025-03-12-1660.

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Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these techniques to continuous domains is challenging due to the curse of dimensionality introduced by discretization. To overcome this limitation, we introduce a quantum Deep Deterministic Policy Gradient (DDPG) algorithm that efficiently addresses both classical and quantum sequential decision problems in continuous action spaces. Moreover, our approach facilitates single-shot quantum state generation: a one-time optimization produces a model that outputs the control sequence required to drive a fixed initial state to any desired target state. In contrast, conventional quantum control methods demand separate optimization for each target state. We demonstrate the effectiveness of our method through simulations and discuss its potential applications in quantum control.

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Pasupuleti, Murali Krishna. "Intelligent Quantum Control Systems Based on Superalgebraic Hamiltonians." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 76–90. https://doi.org/10.62311/nesx/rp0625.

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This paper presents a novel framework for intelligent quantum control that integrates deep reinforcement learning with the algebraic rigor of superalgebraic Hamiltonians derived from Lie superalgebras such as osp(1∣2) and sl(2∣1). Traditional quantum control approaches often fail to respect the rich internal symmetries present in supersymmetric systems, leading to suboptimal or physically inconsistent strategies. To address this, we design a structure-aware control system that embeds symbolic algebraic constraints—such as graded commutation relations and Casimir invariants—directly into the learning architecture. A deep reinforcement learning agent interacts with a simulated quantum environment governed by these constraints, learning control policies that preserve symmetry while optimizing for target state evolution. Experimental results show high fidelity, energy efficiency, and robustness, with successful emulation on real quantum hardware. This research establishes a scalable, interpretable approach to quantum control that is both mathematically principled and practically viable, paving the way for autonomous control in advanced quantum technologies. Keywords: quantum control, Lie superalgebras, superalgebraic Hamiltonians, symbolic computation, reinforcement learning, symmetry-preserving control, osp(1∣2), sl(2∣1), Casimir invariants, quantum machine learning, symmetry-aware reinforcement learning, quantum systems simulation, quantum automation

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Andrés, Eva, Manuel Pegalajar Cuéllar, and Gabriel Navarro. "Brain-Inspired Agents for Quantum Reinforcement Learning." Mathematics 12, no. 8 (2024): 1230. http://dx.doi.org/10.3390/math12081230.

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In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates quantum spiking neural networks (QSNN) and quantum long short-term memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conducted a comparative analysis of our model against existing quantum models, including quantum neural networks (QNNs), QLSTM, QSNN and their classical counterparts, elucidating its unique contributions. Through empirical results, we demonstrated the effectiveness of utilizing quantum models inspired by the brain, which outperform the classical approaches and other quantum models in optimizing energy use case. Specifically, in terms of average, best and worst total reward, test reward, robustness, and learning curve.

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Andrés, Eva, Manuel Pegalajar Cuéllar, and Gabriel Navarro. "On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios." Energies 15, no. 16 (2022): 6034. http://dx.doi.org/10.3390/en15166034.

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In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned.

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Chen, Samuel Yen-Chi. "Asynchronous training of quantum reinforcement learning." Procedia Computer Science 222 (2023): 321–30. http://dx.doi.org/10.1016/j.procs.2023.08.171.

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Koutromanos, Dimitris, Dionisis Stefanatos, and Emmanuel Paspalakis. "Control of Qubit Dynamics Using Reinforcement Learning." Information 15, no. 5 (2024): 272. http://dx.doi.org/10.3390/info15050272.

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The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.

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CHEN, C. L., D. Y. DONG, and Z. H. CHEN. "QUANTUM COMPUTATION FOR ACTION SELECTION USING REINFORCEMENT LEARNING." International Journal of Quantum Information 04, no. 06 (2006): 1071–83. http://dx.doi.org/10.1142/s0219749906002419.

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This paper proposes a novel action selection method based on quantum computation and reinforcement learning (RL). Inspired by the advantages of quantum computation, the state/action in a RL system is represented with quantum superposition state. The probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. And the action selection is carried out by observing quantum state according to collapse postulate of quantum measurement. The results of simulated experiments show that quantum computation can be effectively used to action selection and decision making through speeding up learning. This method also makes a good tradeoff between exploration and exploitation for RL using probability characteristics of quantum theory.

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Olivares-Sánchez, Julio, Jorge Casanova, Enrique Solano, and Lucas Lamata. "Measurement-Based Adaptation Protocol with Quantum Reinforcement Learning in a Rigetti Quantum Computer." Quantum Reports 2, no. 2 (2020): 293–304. http://dx.doi.org/10.3390/quantum2020019.

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We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits.

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Cherrat, El Amine, Snehal Raj, Iordanis Kerenidis, et al. "Quantum Deep Hedging." Quantum 7 (November 29, 2023): 1191. http://dx.doi.org/10.22331/q-2023-11-29-1191.

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Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to 16 qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.

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Nautrup, Hendrik Poulsen, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, and Nicolai Friis. "Optimizing Quantum Error Correction Codes with Reinforcement Learning." Quantum 3 (December 16, 2019): 215. http://dx.doi.org/10.22331/q-2019-12-16-215.

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Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. However, determining the most suited encoding for unknown error channels or specific laboratory setups is highly challenging. Here, we present a reinforcement learning framework for optimizing and fault-tolerantly adapting quantum error correction codes. We consider a reinforcement learning agent tasked with modifying a family of surface code quantum memories until a desired logical error rate is reached. Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest. Moreover, we show that agents trained on one setting are able to successfully transfer their experience to different settings. This ability for transfer learning showcases the inherent strengths of reinforcement learning and the applicability of our approach for optimization from off-line simulations to on-line laboratory settings.

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Cheng, Zhihao, Kaining Zhang, Li Shen, and Dacheng Tao. "Offline Quantum Reinforcement Learning in a Conservative Manner." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7148–56. http://dx.doi.org/10.1609/aaai.v37i6.25872.

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Recently, to reap the quantum advantage, empowering reinforcement learning (RL) with quantum computing has attracted much attention, which is dubbed as quantum RL (QRL). However, current QRL algorithms employ an online learning scheme, i.e., the policy that is run on a quantum computer needs to interact with the environment to collect experiences, which could be expensive and dangerous for practical applications. In this paper, we aim to solve this problem in an offline learning manner. To be more specific, we develop the first offline quantum RL (offline QRL) algorithm named CQ2L (Conservative Quantum Q-learning), which learns from offline samples and does not require any interaction with the environment. CQ2L utilizes variational quantum circuits (VQCs), which are improved with data re-uploading and scaling parameters, to represent Q-value functions of agents. To suppress the overestimation of Q-values resulting from offline data, we first employ a double Q-learning framework to reduce the overestimation bias; then a penalty term that encourages generating conservative Q-values is designed. We conduct abundant experiments to demonstrate that the proposed method CQ2L can successfully solve offline QRL tasks that the online counterpart could not.

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Cárdenas-López, F. A., L. Lamata, J. C. Retamal, and E. Solano. "Multiqubit and multilevel quantum reinforcement learning with quantum technologies." PLOS ONE 13, no. 7 (2018): e0200455. http://dx.doi.org/10.1371/journal.pone.0200455.

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Cheng, Yiming, Yixi Yin, Jiaying Lin, and Yaobin Wang. "Application of Deep Reinforcement Learning in Quantum Control." Journal of Physics: Conference Series 2504, no. 1 (2023): 012022. http://dx.doi.org/10.1088/1742-6596/2504/1/012022.

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Abstract Machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control.

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Moll, Maximilian, and Leonhard Kunczik. "Comparing quantum hybrid reinforcement learning to classical methods." Human-Intelligent Systems Integration 3, no. 1 (2021): 15–23. http://dx.doi.org/10.1007/s42454-021-00025-3.

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AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

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Chen, Junliang, Xianchao Zhang, Fengsong Sun, and Jun Lu. "Knowledge Graph Reasoning with Quantum-Inspired Reinforcement Learning." Chinese Journal of Information Fusion 2, no. 2 (2025): 144–56. https://doi.org/10.62762/cjif.2025.552445.

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Knowledge reasoning is a critical task in information fusion systems, and its core step is reasoning missing information from existing facts to improve the knowledge graphs. Embedding-based reasoning methods and path-based reasoning methods are two mainstream knowledge reasoning methods. Embedding-based reasoning methods enable fast and direct reasoning but are limited to simple relationships between entities and exhibit poor performance in reasoning complex logical relationships. Path-based reasoning methods perform better in complex reasoning tasks, but suffer from high computational complexity, a large number of model parameters, and low reasoning efficiency. To address the aforementioned issues, this paper introduces a knowledge reasoning model called Quantum-Inspired Reinforcement Learning (QIRL). QIRL leverages quantum reinforcement learning to train a strategy network via a quantum circuit, aiming to generate and optimize reasoning paths. Quantum circuit achieves complex nonlinear operations through limited quantum reasoning paths gate operations, reducing computational complexity. In addition, this article utilizes the quantum entanglement property to encode high-dimensional data, reducing the number of model training parameters. This article evaluates the QIRL method on entity prediction task and proves that the QIRL method can effectively reduce the number of model training parameters.

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Wan, Zongqi, Zhijie Zhang, Tongyang Li, Jialin Zhang, and Xiaoming Sun. "Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10087–94. http://dx.doi.org/10.1609/aaai.v37i8.26202.

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Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon T suffer from the regret of at least the square root of T. In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with the order of the polylog T regrets, exponentially improving the dependence in terms of T. To the best of our knowledge, this is the first provable quantum speedup for regrets of bandit problems and in general exploitation in reinforcement learning. Compared to previous literature on quantum exploration algorithms for MAB and reinforcement learning, our quantum input model is simpler and only assumes quantum oracles for each individual arm.

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Barbosa, Diogo, Le Gruenwald, Laurent D’Orazio, and Jorge Bernardino. "QRLIT: Quantum Reinforcement Learning for Database Index Tuning." Future Internet 16, no. 12 (2024): 439. http://dx.doi.org/10.3390/fi16120439.

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Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database management, such as query optimization, transaction scheduling, and index tuning. Promising results have also been seen when reinforcement learning is applied for database tuning in classical computing. However, there is no existing research with implementation details and experiment results for index tuning that takes advantage of both quantum computing and reinforcement learning. This paper proposes a new algorithm called QRLIT that uses the power of quantum computing and reinforcement learning for database index tuning. Experiments using the database TPC-H benchmark show that QRLIT exhibits superior performance and a faster convergence compared to its classical counterpart.

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Dong, Daoyi, Chunlin Chen, Chenbin Zhang, and Zonghai Chen. "Quantum robot: structure, algorithms and applications." Robotica 24, no. 4 (2006): 513–21. http://dx.doi.org/10.1017/s0263574705002596.

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A brand-new paradigm of robots–quantum robots–is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O($N\sqrt N)$. Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.

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Hu, Wei, and James Hu. "Distributional Reinforcement Learning with Quantum Neural Networks." Intelligent Control and Automation 10, no. 02 (2019): 63–78. http://dx.doi.org/10.4236/ica.2019.102004.

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Chen, Samuel Yen-Chi, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Hsi-Sheng Goan. "Variational Quantum Circuits for Deep Reinforcement Learning." IEEE Access 8 (2020): 141007–24. http://dx.doi.org/10.1109/access.2020.3010470.

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An, Zheng, and D. L. Zhou. "Deep reinforcement learning for quantum gate control." EPL (Europhysics Letters) 126, no. 6 (2019): 60002. http://dx.doi.org/10.1209/0295-5075/126/60002.

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Hu, Wei, and James Hu. "Reinforcement Learning with Deep Quantum Neural Networks." Journal of Quantum Information Science 09, no. 01 (2019): 1–14. http://dx.doi.org/10.4236/jqis.2019.91001.

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Li, Ji-An, Daoyi Dong, Zhengde Wei, et al. "Quantum reinforcement learning during human decision-making." Nature Human Behaviour 4, no. 3 (2020): 294–307. http://dx.doi.org/10.1038/s41562-019-0804-2.

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FAKHARI, PEGAH, KARTHIKEYAN RAJAGOPAL, S. N. BALAKRISHNAN, and J. R. BUSEMEYER. "QUANTUM INSPIRED REINFORCEMENT LEARNING IN CHANGING ENVIRONMENT." New Mathematics and Natural Computation 09, no. 03 (2013): 273–94. http://dx.doi.org/10.1142/s1793005713400073.

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Inspired by quantum theory and reinforcement learning, a new framework of learning in unknown probabilistic environment is proposed. Several simulated experiments are given; the results demonstrate the robustness of the new algorithm for some complex problems. Also we generalized the Grover algorithm to improve the rate of converging to an optimal path. In other words, the new generalized algorithm helps to increase the probability of selecting good actions with better weights' adjustments.

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Silva, Agustin, Omar Gustavo Zabaleta, and Constancio Miguel Arizmendi. "QESRL: Exploring Selfish Reinforcement Learning for Repeated Quantum Games." Journal of Physics: Conference Series 2701, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2701/1/012029.

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Abstract A novel unified learning algorithm that seamlessly applies to both classical and quantum non-zero sum games is presented. Building upon the exploring selfish reinforcement learning (ESRL) framework previously proposed in the context of classical games, we extend this approach to handle quantum games with imperfect information. A comparison is made between performance and fairness among agents learning using plain QRL vs. QESRL. The latter enables agents to explore and learn periodic policy strategies in quantum games, leveraging the quantization of games to uncover fairer results. By addressing the challenges posed by the expanded strategy space in quantum games, we test the algorithm’s scalability by increasing the number of agents. Empirical evidence is provided to showcase its performance and to compare classical and quantum game scenarios. The proposed learning algorithm represents a significant step towards understanding the convergence and optimality of strategies in non-zero sum games across classical and quantum settings, bringing us closer to harnessing the potential of independent reinforcement learning and quantum computing in game theory applications.

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Kimura, Tomoaki, Kodai Shiba, Chih-Chieh Chen, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. "Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation." Mathematical Problems in Engineering 2021 (December 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/3511029.

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Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.

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Yoshida, Hibiki, Katsuyoshi Sakamoto, Naoya Miyashita, et al. "Ultrafast inverse design of quantum dot optical spectra via a joint TD-DFT learning scheme and deep reinforcement learning." AIP Advances 12, no. 11 (2022): 115316. http://dx.doi.org/10.1063/5.0127546.

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Here, we report a case study on inverse design of quantum dot optical spectra using a deep reinforcement learning algorithm for the desired target optical property of semiconductor Cd xSe yTe x− y quantum dots. Machine learning models were trained to predict the optical absorption and emission spectra by using the training dataset by time dependent density functional theory simulation. We show that the trained deep deterministic policy gradient inverse design agent can infer the molecular structure with an accuracy of less than 1 Å at a fixed computational time of milliseconds and up to 100–1000 times faster than the conventional heuristic particle swam optimization method. Most of the effective inverse design problems based on the surrogate machine learning and reinforcement learning model have been focused on the field of nano-photonics. Few attempts have been made in the field of quantum optical system in a similar manner. For the first time, our results, to our knowledge, provide concrete evidence that for computationally challenging tasks, a well-trained deep reinforcement learning agent can replace the existing quantum simulation and heuristics optimization tool, enabling fast and scalable simulations of the optical property of nanometer sized semiconductor quantum dots.

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35

Manasi, Sharma. "Quantum Reinforcement Learning for Data-Driven Decision-Making in Autonomous Systems." International Journal of Novel Research and Development 9, no. 11 (2024): b429—b442. https://doi.org/10.5281/zenodo.14263962.

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Quantum Reinforcement Learning (QRL) is a cutting-edge field combining quantum computing with reinforcement learning (RL), aiming to improve the decision-making capabilities of autonomous systems. By exploiting quantum principles like superposition and entanglement, QRL offers notable improvements over classical RL in areas such as computational speed, accuracy, and energy efficiency. This paper presents detailed empirical results, simulations, and theoretical models demonstrating the superiority of QRL over classical methods, particularly in high-complexity environments like autonomous drones and self-driving cars. We also explore the trade-offs, including computational cost, hardware limitations, and energy consumption, providing a balanced view of QRL's potential and limitations for real-world applications.

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Sogabe, Tomah, Tomoaki Kimura, Chih-Chieh Chen, et al. "Model-Free Deep Recurrent Q-Network Reinforcement Learning for Quantum Circuit Architectures Design." Quantum Reports 4, no. 4 (2022): 380–89. http://dx.doi.org/10.3390/quantum4040027.

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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.

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Fikadu Tilaye, Getahun, and Amit Pandey. "Investigating the Effects of Hyperparameters in Quantum-Enhanced Deep Reinforcement Learning." Quantum Engineering 2023 (March 14, 2023): 1–16. http://dx.doi.org/10.1155/2023/2451990.

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Quantum machine learning uses quantum mechanical concepts of superposition of states to make the decision. In this work, we used these quantum advantages to enhance deep reinforcement learning (DRL). Our primary and foremost goal is to investigate and elucidate a way of representing and solving the frozen lake problems by using PennyLane which contains Xanadu’s back-end quantum processing unit. This paper specifically discusses how to enhance classical deep reinforcement learning algorithms with quantum computing technology, making quantum agents get a maximum reward after a fixed number of epochs and realizing the effect of a number of variational quantum layers on the trainability of enhanced framework. We have analyzed that, as the number of layers increases, the ability of the quantum agent to converge to the optimal state also increases. For this work, we have trained the framework agent with 2, 3, and 5 variational quantum layers. An agent with 2 layers converges to a total reward of 0.95 after the training episode of 526. The other agent with layers converges to a total reward of 0.95 after the training episode of 397 and the agent which uses 5 quantum variational layers converges to a total reward of 0.95 after the training episode of 72. From this, we can understand that the agent with a more variational layer exploits more and converges to the optimal state before the other agent. We also analyzed our work in terms of different learning rate hyperparameters. We recorded every single learning epoch to demonstrate the outcomes of enhanced DRL algorithms with selected 0.1, 0.2, 0.3, and 0.4 learning rates or alpha values. From this result, we can conclude that the greater the learning rate values in quantum deep reinforcement learning, the fewer timesteps it takes to move from the start point to the goal state.

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Ying, Ming-Sheng, Yuan Feng, and Sheng-Gang Ying. "Optimal Policies for Quantum Markov Decision Processes." International Journal of Automation and Computing 18, no. 3 (2021): 410–21. http://dx.doi.org/10.1007/s11633-021-1278-z.

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AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

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39

Saggio, V., B. E. Asenbeck, A. Hamann, et al. "Experimental quantum speed-up in reinforcement learning agents." Nature 591, no. 7849 (2021): 229–33. http://dx.doi.org/10.1038/s41586-021-03242-7.

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Albarrán-Arriagada, F., J. C. Retamal, E. Solano, and L. Lamata. "Reinforcement learning for semi-autonomous approximate quantum eigensolver." Machine Learning: Science and Technology 1, no. 1 (2020): 015002. http://dx.doi.org/10.1088/2632-2153/ab43b4.

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Sweke, Ryan, Markus S. Kesselring, Evert P. L. van Nieuwenburg, and Jens Eisert. "Reinforcement learning decoders for fault-tolerant quantum computation." Machine Learning: Science and Technology 2, no. 2 (2021): 025005. http://dx.doi.org/10.1088/2632-2153/abc609.

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42

Daoyi Dong, Chunlin Chen, Jian Chu, and Tzyh-Jong Tarn. "Robust Quantum-Inspired Reinforcement Learning for Robot Navigation." IEEE/ASME Transactions on Mechatronics 17, no. 1 (2012): 86–97. http://dx.doi.org/10.1109/tmech.2010.2090896.

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Neumann, Niels M. P., Paolo B. U. L. de Heer, and Frank Phillipson. "Quantum reinforcement learning." Quantum Information Processing 22, no. 2 (2023). http://dx.doi.org/10.1007/s11128-023-03867-9.

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AbstractIn this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach. We extended these three approaches by allowing for stochastic actions instead of deterministic actions and by introducing a new learning technique called curriculum learning. With curriculum learning, we gradually increase the complexity of the environment and we find that it has a positive effect on the expected reward of a traversal. We see that the number of training steps needed for the two quantum approaches is lower than that needed for the classical approach.

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Patel, Yash J., Sofiene Jerbi, Thomas Bäck, and Vedran Dunjko. "Reinforcement Learning Assisted Recursive QAOA." EPJ Quantum Technology 11, no. 6 (2024). https://doi.org/10.1140/epjqt/s40507-023-00214-w.

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In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope ofusing NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance.To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOAhas been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.

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Yu, Haixu, Xudong Zhao, and Chunlin Chen. "Quantum-Inspired Reinforcement Learning for Quantum Control." IEEE Transactions on Control Systems Technology, 2024, 1–16. http://dx.doi.org/10.1109/tcst.2024.3437142.

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Wauters, Matteo M., Emanuele Panizon, Glen B. Mbeng, and Giuseppe E. Santoro. "Reinforcement-learning-assisted quantum optimization." Physical Review Research 2, no. 3 (2020). http://dx.doi.org/10.1103/physrevresearch.2.033446.

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Xiao, Tailong, Jingzheng Huang, Hongjing Li, Fan Jianping, and Guihua Zeng. "Quantum Generative Adversarial Imitation Learning." New Journal of Physics, March 21, 2023. http://dx.doi.org/10.1088/1367-2630/acc605.

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Abstract Investigating quantum advantage in the NISQ era is a challenging problem whereas quantum machine learning becomes the most promising application that can be resorted to. However, no proposal has been investigated for arguably challenging inverse reinforcement learning to demonstrate the potential advantage. In this work, we propose a hybrid quantum-classical inverse reinforcement learning algorithm based on the variational quantum circuit with the generative adversarial framework. We find an important connection between the quantum gradient anomaly and the performance degradation, which suggest a gradient clipping strategy to stabilize the training process. In light of the algorithm, we study three classic control problems and the Hamiltonian parameter estimation in quantum sensing with shallow quantum circuits. The numerical results showcase that the control-enhanced quantum sensor can saturate quantum Cram'er-Rao bound only with a single variational layer, empirically demonstrating a parameter complexity advantage over the classical learning control. The proposed generative adversarial reinforcement learning algorithm achieves state-of-the-art performance in classical and quantum sensor control in terms of required number of parameters.

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Moro, Lorenzo, Matteo G. A. Paris, Marcello Restelli, and Enrico Prati. "Quantum compiling by deep reinforcement learning." Communications Physics 4, no. 1 (2021). http://dx.doi.org/10.1038/s42005-021-00684-3.

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AbstractThe general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation as a sequence of elements selected from a finite base of universal quantum gates. The Solovay-Kitaev theorem guarantees the existence of such an approximating sequence. Though, the solutions to the quantum compiling problem suffer from a tradeoff between the length of the sequences, the precompilation time, and the execution time. Traditional approaches are time-consuming, unsuitable to be employed during computation. Here, we propose a deep reinforcement learning method as an alternative strategy, which requires a single precompilation procedure to learn a general strategy to approximate single-qubit unitaries. We show that this approach reduces the overall execution time, improving the tradeoff between the length of the sequence and execution time, potentially allowing real-time operations.

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Patel, Yash J., Sofiene Jerbi, Thomas Bäck, and Vedran Dunjko. "Reinforcement learning assisted recursive QAOA." EPJ Quantum Technology 11, no. 1 (2024). http://dx.doi.org/10.1140/epjqt/s40507-023-00214-w.

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AbstractIn recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling -hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.

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50

Ayanzadeh, Ramin, Milton Halem, and Tim Finin. "Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata." Scientific Reports 10, no. 1 (2020). http://dx.doi.org/10.1038/s41598-020-64078-1.

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Journal articles on the topic 'Quantum Reinforcement Learning'

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