Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
27,259
result(s) for
"Huang, Yuan"
Sort by:
فنون شينجيانغ
يأخذ هذا الكتاب (فنون شينجيانغ) للمؤلف (هوانغ شي يوان) القارئ للتعرف على الملاحم القومية التقليدية والرقصات الشعبية والحرف اليدوية القومية بشينجيانغ من منظور الشخص المتكلم، معتمدا على أسلوب النثر الثقافي. حيث يتناول تلك الجوانب بشكل متكامل يجمع بين الرؤية والسماع، وكأنما يأخذ بأيدي القراء حقيقة ليلامسوا المناظر والمشاهد الشعبية بشينجيانغ.
Book
Predicting many properties of a quantum system from very few measurements
Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical shadow’, can be used to predict many different properties; order
log
(
M
)
measurements suffice to accurately predict
M
different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables and the energy variance of many-body local Hamiltonians. The numerical results highlight the advantages of classical shadows relative to previously known methods.
An efficient method has been proposed through which the properties of a complex, large-scale quantum system can be predicted without fully characterizing the quantum state.
Journal Article
Near-term quantum algorithms for linear systems of equations with regression loss functions
Solving linear systems of equations is essential for many problems in science and technology, including problems in machine learning. Existing quantum algorithms have demonstrated the potential for large speedups, but the required quantum resources are not immediately available on near-term quantum devices. In this work, we study near-term quantum algorithms for linear systems of equations, with a focus on the two-norm and Tikhonov regression settings. We investigate the use of variational algorithms and analyze their optimization landscapes. There exist types of linear systems for which variational algorithms designed to avoid barren plateaus, such as properly-initialized imaginary time evolution and adiabatic-inspired optimization, suffer from a different plateau problem. To circumvent this issue, we design near-term algorithms based on a core idea: the classical combination of variational quantum states (CQS). We exhibit several provable guarantees for these algorithms, supported by the representation of the linear system on a so-called ansatz tree. The CQS approach and the ansatz tree also admit the systematic application of heuristic approaches, including a gradient-based search. We have conducted numerical experiments solving linear systems as large as 2 300 × 2 300 by considering cases where we can simulate the quantum algorithm efficiently on a classical computer. Our methods may provide benefits for solving linear systems within the reach of near-term quantum devices.
Journal Article
Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits.
Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.
Journal Article
Exploring the implementation of artificial intelligence applications among academic libraries in Taiwan
PurposeThis study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation.Design/methodology/approachThe author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians.FindingsThe author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI.Research limitations/implicationsThe sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference.Practical implicationsThe results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns.Originality/valueThis study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.
Journal Article
The complexity of NISQ
The recent proliferation of NISQ devices has made it imperative to understand their power. In this work, we define and study the complexity class NISQ, which encapsulates problems that can be efficiently solved by a classical computer with access to noisy quantum circuits. We establish super-polynomial separations in the complexity among classical computation, NISQ, and fault-tolerant quantum computation to solve some problems based on modifications of Simon’s problems. We then consider the power of NISQ for three well-studied problems. For unstructured search, we prove that NISQ cannot achieve a Grover-like quadratic speedup over classical computers. For the Bernstein-Vazirani problem, we show that NISQ only needs a number of queries logarithmic in what is required for classical computers. Finally, for a quantum state learning problem, we prove that NISQ is exponentially weaker than classical computers with access to noiseless constant-depth quantum circuits.
Our current understanding of the computational abilities of near-intermediate scale quantum (NISQ) computing devices is limited, in part due to the absence of a precise definition for this regime. Here, the authors formally define the NISQ realm and provide rigorous evidence that its capabilities are situated between the complexity classes BPP and BQP.
Journal Article
Neutrinophilic axion-like dark matter
The axion-like particles (ALPs) are very good candidates of the cosmological dark matter, which can exist in many extensions of the standard model (SM). The mass of the ALPs can be as small as \\[{\\mathcal {O}}(10^{-22})~\\mathrm{eV}\\]. On the other hand, the neutrinos are found to be massive and the SM must be extended to explain the sub-eV neutrino masses. It becomes very interesting to consider an exclusive coupling between these two low scale frontiers that are both beyond the SM. The propagation of neutrinos inside the Milky Way would undergo the coherent forward scattering effect with the ALP background, and the neutrino oscillation behavior can be modified by the ALP-induced potential. Assuming a derivative coupling between the ALP and the three generations of active neutrinos, possible impacts on the neutrino oscillation experiments have been explored in this paper. In particular, we have numerically studied the sensitivity of the deep underground neutrino experiment (DUNE). The astrophysical consequences of such coupling have also been investigated systematically.
Journal Article
Neutrino-antineutrino asymmetry of CνB on the surface of the round Earth
A
bstract
It has been claimed that the coherent scattering of relic neutrinos with the Earth will result in a neutrino-antineutrino asymmetry of
O
(10
−4
) on the Earth surface, which is five orders of magnitude larger than the naive model expectation. In this work we show that this overdensity was overestimated for the perfectly round Earth by solving the exact solution with partial waves. After summing over all the angular modes, the maximal asymmetry is only around 10
−8
above the ground. To achieve the proposed asymmetry of
O
(10
−4
), a special geography may be needed as the experimental site. The code for a fast evaluation of Bessel functions with large orders through asymptotic expansions has been attached along with this work as supplementary material.
Journal Article
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number
N
of training data points. We show that the generalization error of a quantum machine learning model with
T
trainable gates scales at worst as
T
/
N
. When only
K
≪
T
gates have undergone substantial change in the optimization process, we prove that the generalization error improves to
K
/
N
. Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data.
The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data.
Journal Article
Traditional uses, phytochemical, pharmacology, quality control and modern applications of two important Chinese medicines from Rosa laevigata Michx.: A review
Rosa laevigata Michx. is an ethnic medicine that have strong biological activities used in the traditional medicine system for the treatment of diabetes, nephropathy, myocardial damage, oxidative damage, liver damage and so on. Currently, The Chinese herb R. laevigata Michx. can be divided into two important medicines: Fructus R. laevigata and Radix R. laevigata , from which approximately 148 chemical components have been isolated, including flavonoids, lignans, polyphenols, steroids, triterpenoids, tannins as well as other components. Pharmacological studies have already confirmed that both of these herbs have antioxidant, anti-inflammatory, antiviral and anti-tumor activities, as well as renal protective, immunomodulatory, lipid-lowering, cardiovascular protective, bacteriostatic, and other pharmacological effects. Toxicological tests and quality control studies revealed the safety and nontoxicity of R. laevigata Michx. Therefore, this paper systematically summarizes the traditional uses, botanical, phytochemical, and pharmacology as well as the quality control and toxicology of Fructus and Radix, which in order to provide a comprehensive reference for its continued research.
Journal Article