We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propos...
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We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2d. 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography(AUPT) protocol of d2+d.1measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate(/8 phase gate), and controlled-NOT gate,respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.
Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods...
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Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods deal with this difficulty by assuming that feature vectors lie in a low-dimensional subspace. Based on the fact that a class in a hyperspectral image may be composed of a number of different groups of materials and mixture of spectral features, we suggest to estimate several lower dimensional random subspaces for the samples within each class. For subspace learning and classification, we propose to exploit the union of random subspaces in a Gaussian Mixture Model. Experimental results, conducted on two real hyperspectral data sets, indicate that the proposed method provides competitive classification results in comparison with other state-of-the-art approaches.
Continuous advances in the areas of sensor networks have made wireless sensor networks (WSNs) attractive for a wide variety of applications, with vastly varying requirements and characteristics. As the data sensed by ...
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In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna inf...
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Projection matrix plays an important role in compressive sensing (CS). Small mutual coherence between a projection matrix and a sparsijying matrix is considered to enhance reconstruction performance in CS. The equiang...
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The current rural medical information construction is still imperfect. Large amount of medica data related to common rural disease is used at very low utilization rate. In order to improve the utilization of these dat...
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ISBN:
(纸本)9781509012572
The current rural medical information construction is still imperfect. Large amount of medica data related to common rural disease is used at very low utilization rate. In order to improve the utilization of these data and to provide help for rural barefoot doctors and rural residents, we developed a distributed implied semantic text retrieval model and improved the weigh calculation formula of the implied semantic text retrieva based on implied semantic text retrieval and distributed computing technology. The experiment results indicate that our retrieval framework can shorten the time of text retrieval and improve the accuracy of text retrieval.
Although the distance between binary codes can be computed fast in Hamming space, linear search is not practical for large scale datasets. Therefore attention has been paid to the efficiency of performing approximate ...
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Although the distance between binary codes can be computed fast in Hamming space, linear search is not practical for large scale datasets. Therefore attention has been paid to the efficiency of performing approximate nearest neighbor search, in which hierarchical clustering trees (HCT) are widely used. However, HCT select cluster centers randomly and build indexes with the entire binary code, this degrades search performance. In this paper, we first propose a new clustering algorithm, which chooses cluster centers on the basis of relative distances and uses a more homogeneous partition of the dataset than HCT has to build the hierarchical clustering trees. Then, we present an algorithm to compress binary codes by extracting distinctive bits according to the standard deviation of each bit. Consequently, a new index is proposed using compressed binary codes based on hierarchical decomposition of binary spaces. Experiments conducted on reference datasets and a dataset of one billion binary codes demonstrate the effectiveness and efficiency of our method.
With the popularity of mobile infrastructures providing higher bandwidth and constant connection to the network from virtually anytime and everywhere,the way people use information resources is radically *** can use m...
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With the popularity of mobile infrastructures providing higher bandwidth and constant connection to the network from virtually anytime and everywhere,the way people use information resources is radically *** can use mobile devices such as mobile phones,pocket PCs,laptops,etc.,to obtain services/applications which are based on the mobile
Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only...
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