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As with any quantum computing platform, semiconductor quantum dot devices require sophisticated hardware and controls for operation.
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2014
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2019
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2019
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A. J. Sigillito, J. C. Loy, D. M. Zajac, M. J. Gullans, L. F. Edge, and J. R. Petta, “Site-Selective Quantum Control in an Isotopically Enriched Si 28 / Si 0.7 Ge 0.3 {}^{28}\mathrm{Si}/{\mathrm{Si}}_{0.7}{\mathrm{Ge}}_{0.3} Quadruple Quantum Dot,” Phys. Rev. Appl. 11
2019
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A. R. Mills, D. M. Zajac, M. J. Gullans, F. J. Schupp, T. M. Hazard, and J. R. Petta, “Shuttling a single charge across a one-dimensional array of silicon quantum dots,” Nature Commun. 10
2019
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C. Volk, A. M. J. Zwerver, U. Mukhopadhyay, P. T. Eendebak, C. J. van Diepen, J. P. Dehollain, T. Hensgens, T. Fujita, C. Reichl, W. Wegscheider, and L. M. K. Vandersypen, “Loading a quantum-dot based "qubyte" register,” npj Quantum Inf. 5
2019
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2019
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S. S. Kalantre, J. P. Zwolak, S. Ragole, X. Wu, N. M. Zimmerman, M. D. Stewart Jr., and J. M. Taylor, “Machine learning techniques for state recognition and auto-tuning in quantum dots,” npj Quantum Inf. 5
2019
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J. D. Teske, S. S. Humpohl, R. Otten, P. Bethke, P. Cerfontaine, J. Dedden, A. Ludwig, A. D. Wieck, and H. Bluhm, “A machine learning approach for automated fine-tuning of semiconductor spin qubits,” Appl. Phys. Lett. 114
2019
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S. Rochette, M. Rudolph, A.-M. Roy, M. J. Curry, G. A. T. Eyck, R. P. Manginell, J. R. Wendt, T. Pluym, S. M. Carr, D. R. Ward, M. P. Lilly, M. S. Carroll, and M. Pioro-Ladrière, “Quantum dots with split enhancement gate tunnel barrier control,” Appl. Phys. Lett. 114
2019
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