Quantum Computing在古典计算机上解决困难的计算任务的显着改进承诺。然而，为实际使用设计量子电路不是琐碎的目标，并且需要专家级知识。为了帮助这一努力，提出了一种基于机器学习的方法来构建量子电路架构。以前的作品已经证明，经典的深度加强学习（DRL）算法可以成功构建量子电路架构而没有编码的物理知识。但是，这些基于DRL的作品不完全在更换设备噪声中的设置，从而需要大量的培训资源来保持RL模型最新。考虑到这一点，我们持续学习，以提高算法的性能。在本文中，我们介绍了深度Q-Learning（PPR-DQL）框架的概率策略重用来解决这个电路设计挑战。通过通过各种噪声模式进行数值模拟，我们证明了具有PPR的RL代理能够找到量子栅极序列，以比从划痕训练的代理更快地生成双量标铃声状态。所提出的框架是一般的，可以应用于其他量子栅极合成或控制问题 - 包括量子器件的自动校准。

Quantum computing (QC) promises significant advantages on certain hard computational tasks over classical computers. However, current quantum hardware, also known as noisy intermediate-scale quantum computers (NISQ), are still unable to carry out computations faithfully mainly because of the lack of quantum error correction (QEC) capability. A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code, and its features, such as the requirement of only nearest-neighboring two-qubit control gates and a large error threshold, make it a leading candidate for scalable quantum computation. Recent developments of machine learning (ML)-based techniques especially the reinforcement learning (RL) methods have been applied to the decoding problem and have already made certain progress. Nevertheless, the device noise pattern may change over time, making trained decoder models ineffective. In this paper, we propose a continual reinforcement learning method to address these decoding challenges. Specifically, we implement double deep Q-learning with probabilistic policy reuse (DDQN-PPR) model to learn surface code decoding strategies for quantum environments with varying noise patterns. Through numerical simulations, we show that the proposed DDQN-PPR model can significantly reduce the computational complexity. Moreover, increasing the number of trained policies can further improve the agent's performance. Our results open a way to build more capable RL agents which can leverage previously gained knowledge to tackle QEC challenges.

在过去的十年中，深入的强化学习（RL）已经取得了长足的进步。同时，最先进的RL算法在培训时间融合方面需要大量的计算预算。最近的工作已经开始通过量子计算的角度来解决这个问题，这有望为几项传统上的艰巨任务做出理论上的速度。在这项工作中，我们研究了一类混合量子古典RL算法，我们共同称为变异量子Q-NETWORKS（VQ-DQN）。我们表明，VQ-DQN方法受到导致学习政策分歧的不稳定性的约束，研究了基于经典模拟的既定结果的重复性，并执行系统的实验以识别观察到的不稳定性的潜在解释。此外，与大多数现有的量子增强学习中现有工作相反，我们在实际量子处理单元（IBM量子设备）上执行RL算法，并研究模拟和物理量子系统之间因实施不足而进行的行为差异。我们的实验表明，与文献中相反的主张相反，与经典方法相比，即使在没有物理缺陷的情况下进行模拟，也不能最终决定是否已知量子方法，也可以提供优势。最后，我们提供了VQ-DQN作为可再现的测试床的强大，通用且经过充分测试的实现，以实现未来的实验。

Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning paradigm: the exploration strategy and the slow learning speed, sometimes known as "the curse of dimensionality". This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuous control - namely Soft Actor-Critic. Specifically, the performance of a Variational Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been investigated by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further promising developments.

近年来，机器学习的巨大进步已经开始对许多科学和技术的许多领域产生重大影响。在本文的文章中，我们探讨了量子技术如何从这项革命中受益。我们在说明性示例中展示了过去几年的科学家如何开始使用机器学习和更广泛的人工智能方法来分析量子测量，估计量子设备的参数，发现新的量子实验设置，协议和反馈策略，以及反馈策略，以及通常改善量子计算，量子通信和量子模拟的各个方面。我们重点介绍了公开挑战和未来的可能性，并在未来十年的一些投机愿景下得出结论。

在这项工作中，我们利用量子深的增强学习作为方法，以在三个模拟的复杂性的模拟环境中为简单的，轮式机器人学习导航任务。我们显示了与经典基线相比，在混合量子古典设置中训练有良好建立的深钢筋学习技术的参数化量子电路的相似性能。据我们所知，这是用于机器人行为的量子机学习（QML）的首次演示。因此，我们将机器人技术建立为QML算法的可行研究领域，此后量子计算和量子机学习是自治机器人技术未来进步的潜在技术。除此之外，我们讨论了当前的方法的限制以及自动机器人量子机学习领域的未来研究方向。

In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent's learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent's policy.

量子机学习（QML）被认为是近术语量子设备最有前途的应用之一。然而，量子机器学习模型的优化呈现出众多挑战，从硬件的缺陷和导航指数上缩放的希尔伯特空间中的缺陷产生了巨大的挑战。在这项工作中，我们评估了深度增强学习中的当代方法的潜力，以增加量子变分电路中的增强基于梯度的优化例程。我们发现强化学习增强了优化器，始终突出噪声环境中的渐变血统。所有代码和备用重量都可用于复制结果或在https://github.com/lockwo/rl_qvc_opt上部署模型。

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policybased methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

For a large number of tasks, quantum computing demonstrates the potential for exponential acceleration over classical computing. In the NISQ era, variable-component subcircuits enable applications of quantum computing. To reduce the inherent noise and qubit size limitations of quantum computers, existing research has improved the accuracy and efficiency of Variational Quantum Algorithm (VQA). In this paper, we explore the various ansatz improvement methods for VQAs at the gate level and pulse level, and classify, evaluate and summarize them.

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

A long-standing challenge in artificial intelligence is lifelong learning. In lifelong learning, many tasks are presented in sequence and learners must efficiently transfer knowledge between tasks while avoiding catastrophic forgetting over long lifetimes. On these problems, policy reuse and other multi-policy reinforcement learning techniques can learn many tasks. However, they can generate many temporary or permanent policies, resulting in memory issues. Consequently, there is a need for lifetime-scalable methods that continually refine a policy library of a pre-defined size. This paper presents a first approach to lifetime-scalable policy reuse. To pre-select the number of policies, a notion of task capacity, the maximal number of tasks that a policy can accurately solve, is proposed. To evaluate lifetime policy reuse using this method, two state-of-the-art single-actor base-learners are compared: 1) a value-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent Q-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy Optimisation (PPO) with or without Long Short-Term Memory layer. By selecting the number of policies based on task capacity, D(R)QN achieves near-optimal performance with 6 policies in a 27-task MDP domain and 9 policies in an 18-task POMDP domain; with fewer policies, catastrophic forgetting and negative transfer are observed. Due to slow, monotonic improvement, PPO requires fewer policies, 1 policy for the 27-task domain and 4 policies for the 18-task domain, but it learns the tasks with lower accuracy than D(R)QN. These findings validate lifetime-scalable policy reuse and suggest using D(R)QN for larger and PPO for smaller library sizes.

强化学习（RL）为解决各种复杂的决策任务提供了新的机会。但是，现代的RL算法，例如，深Q学习是基于深层神经网络，在Edge设备上运行时的计算成本很高。在本文中，我们提出了QHD，一种高度增强的学习，它模仿了大脑特性，以实现健壮和实时学习。 QHD依靠轻巧的大脑启发模型来学习未知环境中的最佳政策。我们首先建立一个新颖的数学基础和编码模块，该模块将状态行动空间映射到高维空间中。因此，我们开发了一个高维回归模型，以近似Q值函数。 QHD驱动的代理通过比较每个可能动作的Q值来做出决定。我们评估了不同的RL培训批量和本地记忆能力对QHD学习质量的影响。我们的QHD也能够以微小的本地记忆能力在线学习，这与培训批量大小一样小。 QHD通过进一步降低记忆容量和批处理大小来提供实时学习。这使得QHD适用于在边缘环境中高效的增强学习，这对于支持在线和实时学习至关重要。我们的解决方案还支持少量的重播批量大小，与DQN相比，该批量的速度为12.3倍，同时确保质量损失最小。我们的评估显示了实时学习的QHD能力，比最先进的Deep RL算法提供了34.6倍的速度和更高的学习质量。

由于数据量增加，金融业的快速变化已经彻底改变了数据处理和数据分析的技术，并带来了新的理论和计算挑战。与古典随机控制理论和解决财务决策问题的其他分析方法相比，解决模型假设的财务决策问题，强化学习（RL）的新发展能够充分利用具有更少模型假设的大量财务数据并改善复杂的金融环境中的决策。该调查纸目的旨在审查最近的资金途径的发展和使用RL方法。我们介绍了马尔可夫决策过程，这是许多常用的RL方法的设置。然后引入各种算法，重点介绍不需要任何模型假设的基于价值和基于策略的方法。连接是用神经网络进行的，以扩展框架以包含深的RL算法。我们的调查通过讨论了这些RL算法在金融中各种决策问题中的应用，包括最佳执行，投资组合优化，期权定价和对冲，市场制作，智能订单路由和Robo-Awaring。

In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large state spaces. W-learning algorithm can naturally solve the competition between multiple single policies in multi-objective environments. However, the tabular version does not scale well to environments with large state spaces. To address this issue, we replace underlying Q-tables with DQN, and propose an addition of W-Networks, as a replacement for tabular weights (W) representations. We evaluate the resulting Deep W-Networks (DWN) approach in two widely-accepted multi-objective RL benchmarks: deep sea treasure and multi-objective mountain car. We show that DWN solves the competition between multiple policies while outperforming the baseline in the form of a DQN solution. Additionally, we demonstrate that the proposed algorithm can find the Pareto front in both tested environments.

强化学习和最近的深度增强学习是解决如Markov决策过程建模的顺序决策问题的流行方法。问题和选择算法和超参数的RL建模需要仔细考虑，因为不同的配置可能需要完全不同的性能。这些考虑因素主要是RL专家的任务;然而，RL在研究人员和系统设计师不是RL专家的其他领域中逐渐变得流行。此外，许多建模决策，例如定义状态和动作空间，批次的大小和批量更新的频率以及时间戳的数量通常是手动进行的。由于这些原因，RL框架的自动化不同组成部分具有重要意义，近年来它引起了很多关注。自动RL提供了一个框架，其中RL的不同组件包括MDP建模，算法选择和超参数优化是自动建模和定义的。在本文中，我们探讨了可以在自动化RL中使用的文献和目前的工作。此外，我们讨论了Autorl中的挑战，打开问题和研究方向。

深度强化学习（RL）导致了许多最近和开创性的进步。但是，这些进步通常以培训的基础体系结构的规模增加以及用于训练它们的RL算法的复杂性提高，而均以增加规模的成本。这些增长反过来又使研究人员更难迅速原型新想法或复制已发表的RL算法。为了解决这些问题，这项工作描述了ACME，这是一个用于构建新型RL算法的框架，这些框架是专门设计的，用于启用使用简单的模块化组件构建的代理，这些组件可以在各种执行范围内使用。尽管ACME的主要目标是为算法开发提供一个框架，但第二个目标是提供重要或最先进算法的简单参考实现。这些实现既是对我们的设计决策的验证，也是对RL研究中可重复性的重要贡献。在这项工作中，我们描述了ACME内部做出的主要设计决策，并提供了有关如何使用其组件来实施各种算法的进一步详细信息。我们的实验为许多常见和最先进的算法提供了基准，并显示了如何为更大且更复杂的环境扩展这些算法。这突出了ACME的主要优点之一，即它可用于实现大型，分布式的RL算法，这些算法可以以较大的尺度运行，同时仍保持该实现的固有可读性。这项工作提出了第二篇文章的版本，恰好与模块化的增加相吻合，对离线，模仿和从演示算法学习以及作为ACME的一部分实现的各种新代理。

本文介绍了用于交易单一资产的双重Q网络算法，即E-MINI S＆P 500连续期货合约。我们使用经过验证的设置作为我们环境的基础，并具有多个扩展。我们的贸易代理商的功能不断扩展，包括其他资产，例如商品，从而产生了四种型号。我们还应对环境条件，包括成本和危机。我们的贸易代理商首先接受了特定时间段的培训，并根据新数据进行了测试，并将其与长期策略（市场）进行了比较。我们分析了各种模型与样本中/样本外性能之间有关环境的差异。实验结果表明，贸易代理人遵循适当的行为。它可以将其政策调整为不同的情况，例如在存在交易成本时更广泛地使用中性位置。此外，净资产价值超过了基准的净值，代理商在测试集中的市场优于市场。我们使用DDQN算法对代理商在金融领域中的行为提供初步见解。这项研究的结果可用于进一步发展。

强化学习目睹了最近在量子编程中的各种任务中的应用。基本的假设是这些任务可以建模为马尔可夫决策过程（MDP）。在这里，我们通过探索量子编程中的两个基本任务的后果来研究该假设的可行性：状态制备和门编译。通过形成离散的MDP，专门针对单量的情况（无论有没有噪声），我们可以通过策略迭代准确地为最佳策略求解。我们找到与最短门序列相对应的最佳路径，以准备状态或编译门，直至某些目标精度。例如，我们发现$ h $和$ t $门的序列长达$ 11 $生产$ \ sim 99 \％$ $ fidelity表格$（ht）^{n} | 0 \ rangle $值高达$ n = 10^{10} $。在存在门噪声的情况下，我们演示了最佳政策如何适应嘈杂的门的影响，以实现更高的状态忠诚度。我们的工作表明，人们可以将离散，随机和马尔可夫的性质强加于连续，确定性和非马克维亚量子演化，并提供理论上的洞察力，以了解为什么可以成功地使用强化学习来找到量子编程中的最佳短门序列。

基于变异方法的量子算法是构建量子溶液的最有前途的方法之一，并在过去几年中发现了无数的应用。尽管具有适应性和简单性，但它们的可扩展性和选择合适的ATZ的选择仍然是主要的挑战。在这项工作中，我们报告了基于嵌套的蒙特卡洛树搜索（MCTS）的算法框架，并与组合多部队的bastit相结合（ CMAB）模型，用于量子电路的自动设计。通过数值实验，我们证明了应用于各种问题的算法，包括量子化学中的地面能量问题，在图上进行量子优化，求解线性方程的系统，并找到编码编码与现有方法相比，用于量子误差检测代码的电路，结果表明我们的电路设计算法可以探索更大的搜索空间并优化较大系统的量子电路，从而显示出多功能性和可扩展性。