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

Engineering Biphoton Wave Packets with an Electromagnetically Induced Grating

We propose to shape biphoton wave packets with an electromagnetically induced grating in a four-level double-$\Lambda$ cold atomic system. We show that the induced hybrid grating plays an essential role in directing the new fields into different angular positions, especially to the zeroth-order diffraction. A number of interesting features appear in the shaped two-photon waveforms. For example, broadening or narrowing the spectrum would be possible in the proposed scheme even without the use of a cavity.Comment: Published in Physical Review A 82, 043814 (2010

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 $O(1)$ {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 $T$ 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 $T$. 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 $4$ features for each example, the trained QEL can classify the test examples that are sampled away from the decision boundaries using $146$ single and two qubits quantum gates with $92\%$ accuracy.Comment: 9 page

Designing and Evaluating a Virtual English Enrichment Course for Improving Chinese Learners’ Communicative Competence in English

The aims of this study were to design and develop an online virtual English Enrichment Course (EEC) to develop Chinese English learners’ communicative competence. This study addressed two research questions. First, it considered whether learners progressed in communicative competence after studying the EEC course, and if so, in which elements. Second, it explored learners’ perceptions of the course features that contributed to developing their communicative competence. The development of the course was supported by a complex educational design model, which integrated communicative language teaching theory, learning theories (complex dynamic systems theory and Vygotsky’s sociocultural theory) and conversation-based communicative computer-assisted language learning. The design-based research study comprised four cycles of development, implementation, analysis and refinement of the course. Approximately eight students from China were invited to attend the EEC course in each research cycle; Research Cycles 3 and 4 additionally involved comparison groups. The effectiveness of the course was examined through comparing students’ progress on pre-tests and post-tests. Students’ perceptions of the EEC course were examined through questionnaires and interviews. The findings showed that the EEC course significantly helped students to improve the sociocultural and interactional elements of communicative competence. Key features identified by the students as helping them to improve their communicative competence included the interactive nature of the course, the specialist knowledge of the invited English speakers concerning the topics and the aim of the course to develop communicative competence. This study contributed to these main areas: development of the concept of communicative competence and of new assessment tools for it, including presentation with a focus on question and answer; development of assessment criteria for sociocultural competence; and pioneering and developing complex educational design theory. The lessons learned about the development of core concepts, theories and practice from extensive iteration in design-based research are shared and the future development of the EEC course is proposed

GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.Comment: WWW 2018 Poste