304 research outputs found
A Hybrid Quantum-Classical Hamiltonian Learning Algorithm
Hamiltonian learning is crucial to the certification of quantum devices and
quantum simulators. In this paper, we propose a hybrid quantum-classical
Hamiltonian learning algorithm to find the coefficients of the Pauli operator
components of the Hamiltonian. Its main subroutine is the practical
log-partition function estimation algorithm, which is based on the minimization
of the free energy of the system. Concretely, we devise a stochastic
variational quantum eigensolver (SVQE) to diagonalize the Hamiltonians and then
exploit the obtained eigenvalues to compute the free energy's global minimum
using convex optimization. Our approach not only avoids the challenge of
estimating von Neumann entropy in free energy minimization, but also reduces
the quantum resources via importance sampling in Hamiltonian diagonalization,
facilitating the implementation of our method on near-term quantum devices.
Finally, we demonstrate our approach's validity by conducting numerical
experiments with Hamiltonians of interest in quantum many-body physics.Comment: 24 page
VSQL: Variational Shadow Quantum Learning for Classification
Classification of quantum data is essential for quantum machine learning and
near-term quantum technologies. In this paper, we propose a new hybrid
quantum-classical framework for supervised quantum learning, which we call
Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes
the classical shadows of quantum data, which fundamentally represent the side
information of quantum data with respect to certain physical observables.
Specifically, we first use variational shadow quantum circuits to extract
classical features in a convolution way and then utilize a fully-connected
neural network to complete the classification task. We show that this method
could sharply reduce the number of parameters and thus better facilitate
quantum circuit training. Simultaneously, less noise will be introduced since
fewer quantum gates are employed in such shadow circuits. Moreover, we show
that the Barren Plateau issue, a significant gradient vanishing problem in
quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the
efficiency of VSQL in quantum classification via numerical experiments on the
classification of quantum states and the recognition of multi-labeled
handwritten digits. In particular, our VSQL approach outperforms existing
variational quantum classifiers in the test accuracy in the binary case of
handwritten digit recognition and notably requires much fewer parameters.Comment: 20 pages. To appear in the Thirty-Fifth AAAI Conference on Artificial
Intelligence (AAAI 2021
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs
becomes computational expensive due to the large number of model parameters.
This hinders RNNs from solving many important computer vision tasks, such as
Action Recognition in Videos and Image Captioning. To overcome this problem, we
propose a compact and flexible structure, namely Block-Term tensor
decomposition, which greatly reduces the parameters of RNNs and improves their
training efficiency. Compared with alternative low-rank approximations, such as
tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only
more concise (when using the same rank), but also able to attain a better
approximation to the original RNNs with much fewer parameters. On three
challenging tasks, including Action Recognition in Videos, Image Captioning and
Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of
both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes
17,388 times fewer parameters than the standard LSTM to achieve an accuracy
improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.Comment: CVPR201
Concentration of Data Encoding in Parameterized Quantum Circuits
Variational quantum algorithms have been acknowledged as a leading strategy
to realize near-term quantum advantages in meaningful tasks, including machine
learning and combinatorial optimization. When applied to tasks involving
classical data, such algorithms generally begin with quantum circuits for data
encoding and then train quantum neural networks (QNNs) to minimize target
functions. Although QNNs have been widely studied to improve these algorithms'
performance on practical tasks, there is a gap in systematically understanding
the influence of data encoding on the eventual performance. In this paper, we
make progress in filling this gap by considering the common data encoding
strategies based on parameterized quantum circuits. We prove that, under
reasonable assumptions, the distance between the average encoded state and the
maximally mixed state could be explicitly upper-bounded with respect to the
width and depth of the encoding circuit. This result in particular implies that
the average encoded state will concentrate on the maximally mixed state at an
exponential speed on depth. Such concentration seriously limits the
capabilities of quantum classifiers, and strictly restricts the
distinguishability of encoded states from a quantum information perspective. We
further support our findings by numerically verifying these results on both
synthetic and public data sets. Our results highlight the significance of
quantum data encoding in machine learning tasks and may shed light on future
encoding strategies.Comment: 26 pages including appendi
Towards a millivolt optical modulator with nano-slot waveguides
We describe a class of modulator design involving slot waveguides and electro-optic polymer claddings. Such geometries enable massive enhancement of index tuning when compared to more conventional geometries. We present a semi-analytic method of predicting the index tuning achievable for a given geometry and electro-optic material. Based on these studies, as well as previous experimental results, we show designs for slot waveguide modulators that, when realized in a Mach-Zehnder configuration, will allow for modulation voltages that are orders of magnitude lower than the state of the art. We also discuss experimental results for nano-slot waveguides
Additive Manufacturing of Sn63Pb37 Component by Micro-coating
AbstractMicro-coating is a novel technology to build near-net component layer by layer, which uses a crucible and nozzle instead of a weld head and wire feeder to supply material compared with shaped metal deposition. A pneumatic system is adopted to adjust liquid metal flow rate and the layer height is controlled by the distance between nozzle and substrate. Height and width of a single channel are measured by confocal microscopy, it is found that the error between numerical results and experiment are 5.5% and 1.1%. Tensile stress vertically to the deposition layers reaches to 40.89Mpa, while tensile stress parallel to the deposition layers gives a value of 43.14Mpa. Yield stress of vertically and parallel to the layer are respectively 34.28Mpa and 35.23Mpa. Specimens exhibit better mechanical properties than casting component, whose tensile stress and yield stress are respectively 36.51Mpa and 29.25Mpa
Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey
Since signet ring cells (SRCs) are associated with high peripheral metastasis
rate and dismal survival, they play an important role in determining surgical
approaches and prognosis, while they are easily missed by even experienced
pathologists. Although automatic diagnosis SRCs based on deep learning has
received increasing attention to assist pathologists in improving the
diagnostic efficiency and accuracy, the existing works have not been
systematically overviewed, which hindered the evaluation of the gap between
algorithms and clinical applications. In this paper, we provide a survey on SRC
analysis driven by deep learning from 2008 to August 2023. Specifically, the
biological characteristics of SRCs and the challenges of automatic
identification are systemically summarized. Then, the representative algorithms
are analyzed and compared via dividing them into classification, detection, and
segmentation. Finally, for comprehensive consideration to the performance of
existing methods and the requirements for clinical assistance, we discuss the
open issues and future trends of SRC analysis. The retrospect research will
help researchers in the related fields, particularly for who without medical
science background not only to clearly find the outline of SRC analysis, but
also gain the prospect of intelligent diagnosis, resulting in accelerating the
practice and application of intelligent algorithms
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