12,459 research outputs found
China as a regulatory state
Market economy models differ in the degree of the power of the government vis-à-vis the market in the economy. Under the classications set forth by Glaeser and Shleifer (2002, 2003), and Djankov et al. (2003), these market models range from those emphasizing low government intervention in the market (private orderings and private litigation through courts) to those where the state is an active participant (regulatory state). This paper, using data from a survey of 3,073 private enterprises in China, constructs an index to quantify the power of the government vis-à-vis the market. Regional government power is found to vary considerably across China's regions. Notably, enterprises located in regions where government exerts more power in the market perform better, suggesting that the regulatory state model of the market economy is appropriate for China.regulatory state; disorder costs; dictatorship costs; market economy models; China's economic reform
Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications
The exploration of quantum algorithms that possess quantum advantages is a
central topic in quantum computation and quantum information processing. One
potential candidate in this area is quantum generative adversarial learning
(QuGAL), which conceptually has exponential advantages over classical
adversarial networks. However, the corresponding learning algorithm remains
obscured. In this paper, we propose the first quantum generative adversarial
learning algorithm-- the quantum multiplicative matrix weight algorithm
(QMMW)-- which enables the efficient processing of fundamental tasks. The
computational complexity of QMMW is polynomially proportional to the number of
training rounds and logarithmically proportional to the input size. The core
concept of the proposed algorithm combines QuGAL with online learning. We
exploit the implementation of QuGAL with parameterized quantum circuits, and
numerical experiments for the task of entanglement test for pure state are
provided to support our claims
Test of CPT symmetry in cascade decays
Cascade mixing provides an elegant place to study the mixing.
We use this idea to study the CPT violation caused by mixing.
An approximation method is adopted to treat the two complex
mixing parameters and . A procedure to extract the parameters
and is suggested. The feasibility of exploring the CPT
violation and determining of and in the future B-factories and
LHC-B is discussed.Comment: Latex, 17 pages, some errors modifie
Implementable Quantum Classifier for Nonlinear Data
In this Letter, we propose a quantum machine learning scheme for the
classification of classical nonlinear data. The main ingredients of our method
are variational quantum perceptron (VQP) and a quantum generalization of
classical ensemble learning. Our VQP employs parameterized quantum circuits to
learn a Grover search (or amplitude amplification) operation with classical
optimization, and can achieve quadratic speedup in query complexity compared to
its classical counterparts. We show how the trained VQP can be used to predict
future data with {query} complexity. Ultimately, a stronger nonlinear
classifier can be established, the so-called quantum ensemble learning (QEL),
by combining a set of weak VQPs produced using a subsampling method. The
subsampling method has two significant advantages. First, all weak VQPs
employed in QEL can be trained in parallel, therefore, the query complexity of
QEL is equal to that of each weak VQP multiplied by . Second, it
dramatically reduce the {runtime} complexity of encoding circuits that map
classical data to a quantum state because this dataset can be significantly
smaller than the original dataset given to QEL. This arguably provides a most
satisfactory solution to one of the most criticized issues in quantum machine
learning proposals. To conclude, we perform two numerical experiments for our
VQP and QEL, implemented by Python and pyQuil library. Our experiments show
that excellent performance can be achieved using a very small quantum circuit
size that is implementable under current quantum hardware development.
Specifically, given a nonlinear synthetic dataset with features for each
example, the trained QEL can classify the test examples that are sampled away
from the decision boundaries using single and two qubits quantum gates
with accuracy.Comment: 9 page
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