Quantum Machine Learning
10 papers for year 2016 from Europe PMC
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.
Showing 1-10 of 10
A modular design of molecular qubits to implement universal quantum gates.
Ferrando-Soria J, Moreno Pineda E, Chiesa A, Fernandez A, Magee SA, Carretta S, Santini P, Vitorica-Yrezabal IJ, Tuna F, Timco GA, McInnes EJ, Winpenny RE.
Computational quantum-classical boundary of noisy commuting quantum circuits.
Fujii K, Tamate S.
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.
Learning robust pulses for generating universal quantum gates.
Dong D, Wu C, Chen C, Qi B, Petersen IR, Nori F.
Measurements of nanoresonator-qubit interactions in a hybrid quantum electromechanical system.
Rouxinol F, Hao Y, Brito F, Caldeira AO, Irish EK, LaHaye MD.
Minimizing resource overheads for fault-tolerant preparation of encoded states of the Steane code.
Goto H.
Quantification of DNA Extracted from Formalin Fixed Paraffin-Embeded Tissue Comparison of Three Techniques: Effect on PCR Efficiency.
Kumar D, Panigrahi MK, Suryavanshi M, Mehta A, Saikia KK.
Quantum Error Correction Protects Quantum Search Algorithms Against Decoherence.
Botsinis P, Babar Z, Alanis D, Chandra D, Nguyen H, Ng SX, Hanzo L.
Semiconductor-inspired design principles for superconducting quantum computing.
Shim YP, Tahan C.
Speedup of quantum evolution of multiqubit entanglement states.
Zhang YJ, Han W, Xia YJ, Tian JX, Fan H.