Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science. Recent developments have shown that a modest number of randomized measurements suffices to learn many properties of a quantum many-body system. However, implementing such measurements requires complete control over individual particles, which is unavailable in many experimental platforms. In this work, we present rigorous and efficient algorithms for learning quantum many-body states in systems with any degree of control over individual particles, including when every particle is subject to the same global field and no additional ancilla particles are available. We numerically demonstrate the effectiveness of our algorithms for estimating energy densities in a U(1) lattice gauge theory and classifying topological order using very limited measurement capabilities.

已经表明，可以使用具有合适的数据访问的经典算法有效地复制一些量子机器学习算法的表观优点 - 一种称为渐变化的过程。现有的追逐工作的工作比较量子算法占据N-qubit Quantum State $ | x \ rangle = \ sum_ {i} x_i | i \ rangle $的副本到具有样本和查询（Sq）访问的经典算法矢量$ x $。在本说明中，我们证明了具有SQ访问的经典算法可以比量子状态输入的量子算法呈指数级速率地实现一些学习任务。因为经典算法是量子算法的子集，所以这表明SQ接入有时可以比量子状态输入更强大。我们的研究结果表明，在某些学习任务中没有指数量子优势可能是由于相对于量子状态输入的SQ访问过于强大。如果我们将量子算法与量子状态的输入进行比较到具有对量子状态上的测量数据的经典算法，则量子优势的景观可以显着不同。

量子技术有可能彻底改变我们如何获取和处理实验数据以了解物理世界。一种实验设置，将来自物理系统的数据转换为稳定的量子存储器，以及使用量子计算机的数据的处理可以具有显着的优点，这些实验可以具有测量物理系统的传统实验，并且使用经典计算机处理结果。我们证明，在各种任务中，量子机器可以从指数较少的实验中学习而不是传统实验所需的实验。指数优势在预测物理系统的预测属性中，对噪声状态进行量子主成分分析，以及学习物理动态的近似模型。在一些任务中，实现指数优势所需的量子处理可能是适度的;例如，可以通过仅处理系统的两个副本来同时了解许多非信息可观察。我们表明，可以使用当今相对嘈杂的量子处理器实现大量超导QUBITS和1300个量子门的实验。我们的结果突出了量子技术如何能够实现强大的新策略来了解自然。

我们研究量子存储器的力量，以了解量子系统和动态的学习性质，这在物理和化学方面具有重要意义。许多最先进的学习算法需要访问额外的外部量子存储器。虽然这种量子存储器不需要先验，但在许多情况下，不利用量子存储器的算法需要比那些更多样的数据。我们表明，这种权衡在各种学习问题中是固有的。我们的结果包括以下内容：（1）我们显示以$ M $ -Qubit状态Rho执行暗影断层扫描，以M $观察到，任何没有量子存储器的算法需要$ \ omega（\ min（m，2 ^ n） ）最坏情况下Rho的标准。达到对数因子，这与[HKP20]的上限匹配，完全解决了[AAR18，AR19]中的打开问题。 （2）我们在具有和不具有量子存储器之间的算法之间建立指数分离，用于纯度测试，区分扰扰和去极化的演变，以及在物理动态中揭示对称性。我们的分离通过允许更广泛的无量子存储器的算法来改善和概括[ACQ21]的工作。 （3）我们提供量子存储器和样本复杂性之间的第一个权衡。我们证明，估计所有$ N $ -Qubit Pauli可观察到的绝对值，Qumum Memory的$ K <N $ Qubits的算法需要至少$ \ omega（2 ^ {（nk）/ 3}）$样本，但在那里是使用$ n $ -Qubit量子存储器的算法，该算法只需要$ o（n）$ samples。我们展示的分离足够大，并且可能已经是显而易见的，例如，数十Qubits。这提供了一种具体的路径，朝着使用量子存储器学习算法的实际优势。

我们证明了能够在$ N $ -Qubit州$ \ Rho $同时的最多$ k $ reporicas上进行纠结的速度，有$ \ rho $的属性，这需要至少订购$ 2 ^ n / k^ 2 $测量学习。但是，相同的属性只需要一个测量来学习，如果我们可以在$ k，n $的k，n $的多个副本多项式上进行纠缠测量。因为上面保持每个正整数$ k $，我们获得了一系列的任务等级，需要有效地执行更多的副本。我们介绍了一种强大的证明技术来建立我们的结果，并用它来提供用于测试量子状态的混合的新界限。

Quantum Machine Learning (QML) shows how it maintains certain significant advantages over machine learning methods. It now shows that hybrid quantum methods have great scope for deployment and optimisation, and hold promise for future industries. As a weakness, quantum computing does not have enough qubits to justify its potential. This topic of study gives us encouraging results in the improvement of quantum coding, being the data preprocessing an important point in this research we employ two dimensionality reduction techniques LDA and PCA applying them in a hybrid way Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) in the classification of Diabetes.

Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly-available datasets that can be used for such training are limited in their size and diversity. To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors. We evaluate six contemporary stylization methods on four publicly-available IR datasets - the first comparison of its kind - and find that CMST is highly effective for DNN-based detectors. Surprisingly, we find that existing data-driven methods are outperformed by a simple grayscale stylization (an average of the color channels). Our analysis reveals that existing data-driven methods are either too simplistic or introduce significant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We find that MLST leads to more complex stylizations without introducing significant image artifacts and achieves the best overall detector performance on our benchmark datasets.

Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. Objective: A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs, including schizophrenia, schizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcare insurance claims and 2) electronic health records (EHRs). Design, setting and participants: Data from beneficiaries from a nationwide commercial healthcare insurer with 77.4 million members and data from patients from EHRs from eight academic hospitals based in the U.S. were used. First, the predictive models were constructed and tested using data in case-control cohorts from insurance claims or EHR data. Second, performance of the predictive models across data sources were analyzed. Third, as an illustrative application, the models were further trained to predict risks of SMIs among 18-year old young adults and individuals with substance associated conditions. Main outcomes and measures: Machine learning-based predictive models for SMIs in the general population were built based on insurance claims and EHR.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

We test grip strength and shock absorption properties of various granular material in granular jamming robotic components. The granular material comprises a range of natural, manufactured, and 3D printed material encompassing a wide range of shapes, sizes, and Shore hardness. Two main experiments are considered, both representing compelling use cases for granular jamming in soft robotics. The first experiment measures grip strength (retention force measured in Newtons) when we fill a latex balloon with the chosen grain type and use it as a granular jamming gripper to pick up a range of test objects. The second experiment measures shock absorption properties recorded by an Inertial Measurement Unit which is suspended in an envelope of granular material and dropped from a set height. Our results highlight a range of shape, size and softness effects, including that grain deformability is a key determinant of grip strength, and interestingly, that larger grain sizes in 3D printed grains create better shock absorbing materials.