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 205-216 of 290
Characterizing large-scale quantum computers via cycle benchmarking.
Erhard A, Wallman JJ, Postler L, Meth M, Stricker R, Martinez EA, Schindler P, Monz T, Emerson J, Blatt R.
Circuit-Based Quantum Random Access Memory for Classical Data.
Park DK, Petruccione F, Rhee JK.
Dynamic Concatenation of Quantum Error Correction in Integrated Quantum Computing Architecture.
Sohn I, Bang J, Heo J.
Experimental Measurement of the Hilbert-Schmidt Distance between Two-Qubit States as a Means for Reducing the Complexity of Machine Learning.
Trávníček V, Bartkiewicz K, Černoch A, Lemr K.
Experimentally attacking quantum money schemes based on quantum retrieval games.
Jiráková K, Bartkiewicz K, Černoch A, Lemr K.
Hardware-Efficient Quantum Random Access Memory with Hybrid Quantum Acoustic Systems.
Hann CT, Zou CL, Zhang Y, Chu Y, Schoelkopf RJ, Girvin SM, Jiang L.
Hybrid quantum linear equation algorithm and its experimental test on IBM Quantum Experience.
Lee Y, Joo J, Lee S.
In Situ Characterization of Qubit Control Lines: A Qubit as a Vector Network Analyzer.
Jerger M, Kulikov A, Vasselin Z, Fedorov A.
Information Geometrical Characterization of Quantum Statistical Models in Quantum Estimation Theory.
Suzuki J.
QuBiT: a quantitative tool for analyzing epithelial tubes reveals unexpected patterns of organization in the <i>Drosophila</i> trachea.
Yang R, Li E, Kwon YJ, Mani M, Beitel GJ.
Remote preparation for single-photon two-qubit hybrid state with hyperentanglement via linear-optical elements.
Jiao XF, Zhou P, Lv SX, Wang ZY.
Revising the measurement process in the variational quantum eigensolver: is it possible to reduce the number of separately measured operators?
Izmaylov AF, Yen TC, Ryabinkin IG.