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John Preskill
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2000
A. M. Childs
,
Preskill, J.
, and
Renes, J.
,
“
Quantum information and precision measurement
”
,
Journal of Modern Optics
, vol. 47, no. 2-3, pp. 155 - 176, 2000.
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2001
A. M. Childs
,
Farhi, E.
, and
Preskill, J.
,
“
Robustness of adiabatic quantum computation
”
,
Physical Review A
, vol. 65, no. 1, 2001.
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2012
S. P. Jordan
,
Lee, K. S. M.
, and
Preskill, J.
,
“
Quantum Algorithms for Quantum Field Theories
”
,
Science
, vol. 336, no. 6085, pp. 1130 - 1133, 2012.
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2014
S. P. Jordan
,
Lee, K. S. M.
, and
Preskill, J.
,
“
Quantum Algorithms for Fermionic Quantum Field Theories
”
, 2014.
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S. P. Jordan
,
Lee, K. S. M.
, and
Preskill, J.
,
“
Quantum Computation of Scattering in Scalar Quantum Field Theories
”
,
Quantum Information and Computation
, vol. 14, no. 11-12, pp. 1014-1080, 2014.
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2018
S. P. Jordan
,
Krovi, H.
,
Lee, K. S. M.
, and
Preskill, J.
,
“
BQP-completeness of Scattering in Scalar Quantum Field Theory
”
,
Quantum
, vol. 2, p. 44, 2018.
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2019
Y. Alexeev
,
Bacon, D.
,
Brown, K. R.
,
Calderbank, R.
,
Carr, L. D.
,
Chong, F. T.
,
DeMarco, B.
,
Englund, D.
,
Farhi, E.
,
Fefferman, B.
,
Gorshkov, A. V.
,
Houck, A.
,
Kim, J.
,
Kimmel, S.
,
Lange, M.
,
Lloyd, S.
,
Lukin, M. D.
,
Maslov, D.
,
Maunz, P.
,
Monroe, C.
,
Preskill, J.
,
Roetteler, M.
,
Savage, M.
,
Thompson, J.
, and
Vazirani, U.
,
“
Quantum Computer Systems for Scientific Discovery
”
, 2019.
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2020
P. Faist
,
Nezami, S.
,
Albert, V. V.
,
Salton, G.
,
Pastawski, F.
,
Hayden, P.
, and
Preskill, J.
,
“
Continuous symmetries and approximate quantum error correction
”
,
Phys. Rev. X
, vol. 10, no. 041018, 2020.
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V. V. Albert
,
Covey, J. P.
, and
Preskill, J.
,
“
Robust Encoding of a Qubit in a Molecule
”
,
Phys. Rev. X
, vol. 10, no. 031050, 2020.
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2021
V. V. Albert
,
Aasen, D.
,
Xu, W.
,
Ji, W.
,
Alicea, J.
, and
Preskill, J.
,
“
Spin chains, defects, and quantum wires for the quantum-double edge
”
, 2021.
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2022
Y. Tong
,
Albert, V. V.
,
McClean, J. R.
,
Preskill, J.
, and
Su, Y.
,
“
Provably accurate simulation of gauge theories and bosonic systems
”
,
Quantum
, vol. 6, p. 816, 2022.
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H. - Y. Huang
,
Kueng, R.
,
Torlai, G.
,
Albert, V. V.
, and
Preskill, J.
,
“
Provably efficient machine learning for quantum many-body problems
”
,
Science
, vol. 377, 2022.
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