与古典应用相比，量子力学的异国大量性质使得量子领域的机器学习（ML）不同。ML可以使用广泛的任务中从量子系统中连续提取的信息用于知识发现。该模型接收用于学习和决策的流量子信息，从而导致对量子系统的即时反馈。作为流的学习方法，我们在存在静物，去除和放松的情况下，从连续测量的Qubit中展示了一个深入的加强学习。我们还调查代理商如何通过转移学习对另一个量子噪声模式适应。流学习提供了更好地理解闭环量子控制，这可能为先进量子技术铺平道路。

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。

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.

In this thesis, we consider two simple but typical control problems and apply deep reinforcement learning to them, i.e., to cool and control a particle which is subject to continuous position measurement in a one-dimensional quadratic potential or in a quartic potential. We compare the performance of reinforcement learning control and conventional control strategies on the two problems, and show that the reinforcement learning achieves a performance comparable to the optimal control for the quadratic case, and outperforms conventional control strategies for the quartic case for which the optimal control strategy is unknown. To our knowledge, this is the first time deep reinforcement learning is applied to quantum control problems in continuous real space. Our research demonstrates that deep reinforcement learning can be used to control a stochastic quantum system in real space effectively as a measurement-feedback closed-loop controller, and our research also shows the ability of AI to discover new control strategies and properties of the quantum systems that are not well understood, and we can gain insights into these problems by learning from the AI, which opens up a new regime for scientific research.

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.

With the development of experimental quantum technology, quantum control has attracted increasing attention due to the realization of controllable artificial quantum systems. However, because quantum-mechanical systems are often too difficult to analytically deal with, heuristic strategies and numerical algorithms which search for proper control protocols are adopted, and, deep learning, especially deep reinforcement learning (RL), is a promising generic candidate solution for the control problems. Although there have been a few successful applications of deep RL to quantum control problems, most of the existing RL algorithms suffer from instabilities and unsatisfactory reproducibility, and require a large amount of fine-tuning and a large computational budget, both of which limit their applicability. To resolve the issue of instabilities, in this dissertation, we investigate the non-convergence issue of Q-learning. Then, we investigate the weakness of existing convergent approaches that have been proposed, and we develop a new convergent Q-learning algorithm, which we call the convergent deep Q network (C-DQN) algorithm, as an alternative to the conventional deep Q network (DQN) algorithm. We prove the convergence of C-DQN and apply it to the Atari 2600 benchmark. We show that when DQN fail, C-DQN still learns successfully. Then, we apply the algorithm to the measurement-feedback cooling problems of a quantum quartic oscillator and a trapped quantum rigid body. We establish the physical models and analyse their properties, and we show that although both C-DQN and DQN can learn to cool the systems, C-DQN tends to behave more stably, and when DQN suffers from instabilities, C-DQN can achieve a better performance. As the performance of DQN can have a large variance and lack consistency, C-DQN can be a better choice for researches on complicated control problems.

基于量子的通信中的当前技术将量子数据的新集成与经典数据进行混合处理。但是，这些技术的框架仅限于单个经典或量子任务，这限制了它们在近期应用中的灵活性。我们建议在需要经典和量子输入的计算任务中利用量子储存器处理器来利用量子动力学。该模拟处理器包括一个量子点网络，其中量子数据被入射到网络中，并且经典数据通过一个连贯的字段刺激了网络进行编码。我们执行量子断层扫描和经典通道非线性均衡的多任务应用。有趣的是，可以通过对经典数据的反馈控制以闭环方式进行断层扫描。因此，如果经典输入来自动力学系统，则将该系统嵌入封闭环中，即使访问对外部经典输入的访问被中断也可以处理混合处理。最后，我们证明准备量子去极化通道是一种用于量子数据处理的新型量子机学习技术。

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。

量子信息技术的快速发展显示了在近期量子设备中模拟量子场理论的有希望的机会。在这项工作中，我们制定了1+1尺寸$ \ lambda \ phi \ phi^4 $量子场理论的（时间依赖性）变异量子模拟理论，包括编码，状态准备和时间演化，并具有多个数值模拟结果。这些算法可以理解为Jordan-Lee-Preskill算法的近期变异类似物，这是使用通用量子设备模拟量子场理论的基本算法。此外，我们强调了基于LSZ降低公式和几种计算效率的谐波振荡器基础编码的优势，例如在实施单一耦合群集ANSATZ的肺泡版本时，以准备初始状态。我们还讨论了如何在量子场理论仿真中规避“光谱拥挤”问题，并根据州和子空间保真度评估我们的算法。

FIG. 1. Schematic diagram of a Variational Quantum Algorithm (VQA). The inputs to a VQA are: a cost function C(θ), with θ a set of parameters that encodes the solution to the problem, an ansatz whose parameters are trained to minimize the cost, and (possibly) a set of training data {ρ k } used during the optimization. Here, the cost can often be expressed in the form in Eq. ( 3), for some set of functions {f k }. Also, the ansatz is shown as a parameterized quantum circuit (on the left), which is analogous to a neural network (also shown schematically on the right). At each iteration of the loop one uses a quantum computer to efficiently estimate the cost (or its gradients). This information is fed into a classical computer that leverages the power of optimizers to navigate the cost landscape C(θ) and solve the optimization problem in Eq. ( 1). Once a termination condition is met, the VQA outputs an estimate of the solution to the problem. The form of the output depends on the precise task at hand. The red box indicates some of the most common types of outputs.

在当前的嘈杂中间尺度量子（NISQ）时代，量子机学习正在成为基于程序门的量子计算机的主要范式。在量子机学习中，对量子电路的门进行了参数化，并且参数是根据数据和电路输出的测量来通过经典优化来调整的。参数化的量子电路（PQC）可以有效地解决组合优化问题，实施概率生成模型并进行推理（分类和回归）。该专着为具有概率和线性代数背景的工程师的观众提供了量子机学习的独立介绍。它首先描述了描述量子操作和测量所必需的必要背景，概念和工具。然后，它涵盖了参数化的量子电路，变异量子本质层以及无监督和监督的量子机学习公式。

旨在在低维潜在空间中压缩量子信息的量子自动编码器位于量子信息领域的自动数据压缩的核心。在本文中，我们为给定的量子自动编码器建立了压缩率的上限，并提出了一种学习控制方法，用于训练自动编码器以达到最大压缩率。理论上使用特征分解和基质分化来证明压缩率的上限，这取决于输入状态的密度矩阵表示的特征值。提出了2 Q量和3 Q量系统的数值结果，以演示如何训练量子自动编码器以实现理论上最大的压缩，并比较使用不同的机器学习算法的训练性能。说明了使用量子光学系统的量子自动编码器的实验结果，以将两个2 Q Q Q Q Qubit的状态压缩为两个1 Quit状态。

Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences. In our work, based on various arguments from complexity theory and physics, we demonstrate that a single Kerr mode can provide some "quantum enhancements" when dealing with kernel-based methods. Using kernel properties, neural tangent kernel theory, first-order perturbation theory of the Kerr non-linearity, and non-perturbative numerical simulations, we show that quantum enhancements could happen in terms of convergence time and generalization error. Furthermore, we make explicit indications on how higher-dimensional input data could be considered. Finally, we propose an experimental protocol, that we call \emph{quantum Kerr learning}, based on circuit QED.

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。

量子计算有可能彻底改变和改变我们的生活和理解世界的方式。该审查旨在提供对量子计算的可访问介绍，重点是统计和数据分析中的应用。我们从介绍了了解量子计算所需的基本概念以及量子和经典计算之间的差异。我们描述了用作量子算法的构建块的核心量子子程序。然后，我们审查了一系列预期的量子算法，以便在统计和机器学习中提供计算优势。我们突出了将量子计算应用于统计问题的挑战和机遇，并讨论潜在的未来研究方向。

量化和验证准备量子状态的控制水平是构建量子器件中的中心挑战。量子状态的特点是实验测量，使用称为断层扫描的程序，这需要大量资源。此外，尚未制定与颞下处理的量子装置的断层扫描，其尚未制定与标准断层扫描的逐时处理。我们使用经常性机器学习框架开发了一种实用和近似的断层扫描方法，用于这种有趣情况。该方法基于具有量子态流称为量子储存器的系统之间的重复量子相互作用。来自储存器的测量数据连接到线性读数，以训练施加到输入流的量子通道之间的反复关系。我们展示了Quantum学习任务的算法，然后是Quantum短期内存容量的提议，以评估近术语量子器件的时间处理能力。

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。

我们提出了一种普遍和系统的策略来编制任意量子信道而不使用辅助额度的辅助额度 - 一种强大的深度加强学习算法。我们严格证明，与编译酉栅极的情况鲜明对比，不管分解序列的长度如何，不可能将任意信道与任意精度编译成任意精度。但是，对于固定精度$ \ epsilon $一个可以用恒定数量的$ \ epsilon $ -dependent基本通道构建通用集，使得任意量子通道可以分解成这些基本通道的序列，然后是酉门，序列长度有$ o（\ frac {1} {\ epsilon} \ log \ frac {1} {\ epsilon}）$。通过一个关于Majorana Fermions的拓扑编译的具体例子，我们表明我们所提出的算法可以通过将加权成本添加到近端政策优化的奖励功能中方便和有效地减少昂贵的基本栅极的使用。

变异量子算法（VQA）在NISQ时代表现出巨大的潜力。在VQA的工作流程中，Ansatz的参数迭代更新以近似所需的量子状态。我们已经看到了各种努力，以较少的大门起草更好的安萨兹。在量子计算机中，栅极Ansatz最终将转换为控制信号，例如TransMons上的微波脉冲。并且对照脉冲需要精心校准，以最大程度地减少误差（例如过度旋转和旋转）。在VQA的情况下，此过程将引入冗余，但是VQAS的变异性能自然可以通过更新幅度和频率参数来处理过度旋转和重组的问题。因此，我们提出了PAN，这是一种用于VQA的天然脉冲ANSATZ GENTARATOR框架。我们生成具有可训练参数用于振幅和频率的天然脉冲ansatz。在我们提出的锅中，我们正在调整参数脉冲，这些脉冲在NISQ计算机上得到了内在支持。考虑到本机 - 脉冲ANSATZ不符合参数迁移规则，我们需要部署非级别优化器。为了限制发送到优化器的参数数量，我们采用了一种生成本机 - 脉冲ANSATZ的渐进式方式。实验是在模拟器和量子设备上进行的，以验证我们的方法。当在NISQ机器上采用时，PAN获得的延迟平均提高了86％。 PAN在H2和HEH+上的VQE任务分别能够达到99.336％和96.482％的精度，即使NISQ机器中有很大的噪声。