Quantum Machine Learning
290 papers 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 253-264 of 290
Hybrid quantum gates between flying photon and diamond nitrogen-vacancy centers assisted by optical microcavities.
Wei HR, Long GL.
Robust quantum control using smooth pulses and topological winding.
Barnes E, Wang X, Das Sarma S.
Toward the Quantum Computer: Magnetic Molecules Back in the Race.
Sessoli R.
Verifiable Measurement-Only Blind Quantum Computing with Stabilizer Testing.
Hayashi M, Morimae T.
Adaptive quantum state estimation of an entangled qubit state.
Lerch S, Stefanov A.
An empirical approach for quantifying loop-mediated isothermal amplification (LAMP) using Escherichia coli as a model system.
Subramanian S, Gomez RD.
Experimental comparison of efficient tomography schemes for a six-qubit state.
Schwemmer C, Tóth G, Niggebaum A, Moroder T, Gross D, Gühne O, Weinfurter H.
Experimental realization of a one-way quantum computer algorithm solving Simon's problem.
Tame MS, Bell BA, Di Franco C, Wadsworth WJ, Rarity JG.
Exponential rise of dynamical complexity in quantum computing through projections.
Burgarth DK, Facchi P, Giovannetti V, Nakazato H, Pascazio S, Yuasa K.
Hybrid architecture for encoded measurement-based quantum computation.
Zwerger M, Briegel HJ, Dür W.
Implementing a strand of a scalable fault-tolerant quantum computing fabric.
Chow JM, Gambetta JM, Magesan E, Abraham DW, Cross AW, Johnson BR, Masluk NA, Ryan CA, Smolin JA, Srinivasan SJ, Steffen M.
Quantum control and process tomography of a semiconductor quantum dot hybrid qubit.
Kim D, Shi Z, Simmons CB, Ward DR, Prance JR, Koh TS, Gamble JK, Savage DE, Lagally MG, Friesen M, Coppersmith SN, Eriksson MA.