Quantum Machine Learning

46 papers for year 2016

Quantum Machine Learning Research Context

This category covers quantum machine learning research, including quantum kernels, variational classifiers, hybrid learning systems, generative models, and QML benchmarks.

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All sources 46 arXiv preprints 30 Crossref 5 Europe PMC 10 DOAJ 1

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Enhancing quantum sensing sensitivity by a quantum memory.

Zaiser S, Rendler T, Jakobi I, Wolf T, Lee SY, Wagner S, Bergholm V, Schulte-Herbrüggen T, Neumann P, Wrachtrup J.

2016 Europe PMC Nat Commun

Evaluation of the capacity of communication lines with nonlinear finite memory

E G Shapiro, D A Shapiro

2016 Crossref Quantum Electronics

Fidelity as a figure of merit in quantum error correction

Jonas Almlöf, Gunnar Björk

2016 arXiv arXiv preprint

Finite blocklength and moderate deviation analysis of hypothesis testing of correlated quantum states and application to classical-quantum channels with memory

Cambyse Rouze, Nilanjana Datta

2016 arXiv arXiv preprint

Formal Series of Generalised Functions and Their Application to Deformation Quantisation

Jaromir Tosiek, Michał Dobrski

2016 arXiv arXiv preprint

Generalized Coherent States, Reproducing Kernels, and Quantum Support Vector Machines

Rupak Chatterjee, Ting Yu

2016 arXiv arXiv preprint

Identifying the minor set cover of dense connected bipartite graphs via random matching edge sets

Kathleen E. Hamilton, Travis S. Humble

2016 arXiv arXiv preprint

Learning robust pulses for generating universal quantum gates.

Dong D, Wu C, Chen C, Qi B, Petersen IR, Nori F.

2016 Europe PMC Sci Rep

Machine Learning Topological States

Dong-Ling Deng, Xiaopeng Li, S. Das Sarma

2016 arXiv arXiv preprint

Measurements of nanoresonator-qubit interactions in a hybrid quantum electromechanical system.

Rouxinol F, Hao Y, Brito F, Caldeira AO, Irish EK, LaHaye MD.

2016 Europe PMC Nanotechnology

Measuring quantum entanglement, machine learning and wave function tomography: Bridging theory and experiment with the quantum gas microscope

Norm M. Tubman

2016 arXiv arXiv preprint

Minimizing resource overheads for fault-tolerant preparation of encoded states of the Steane code.

Goto H.

2016 Europe PMC Sci Rep

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