Quantum advantage in learning from experiments

被引:236
作者
Huang, Hsin-Yuan [1 ,2 ]
Broughton, Michael [3 ]
Cotler, Jordan [4 ,5 ]
Chen, Sitan [6 ,7 ]
Li, Jerry [8 ]
Mohseni, Masoud [3 ]
Neven, Hartmut [3 ]
Babbush, Ryan [3 ]
Kueng, Richard [9 ]
Preskill, John [1 ,2 ,10 ]
McClean, Jarrod R. [3 ]
机构
[1] CALTECH, Inst Quantum Informat & Matter, Pasadena, CA 91125 USA
[2] CALTECH, Dept Comp & Math Sci, Pasadena, CA 91125 USA
[3] Google Quantum AI, Venice, CA 90291 USA
[4] Harvard Soc Fellows, Cambridge, MA 02138 USA
[5] Black Hole Initiat, Cambridge, MA 02138 USA
[6] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
[7] Simons Inst Theory Comp, Berkeley, CA USA
[8] Microsoft Res AI, Redmond, WA 98052 USA
[9] Johannes Kepler Univ Linz, Inst Integrated Circuits, Linz, Austria
[10] AWS Ctr Quantum Comp, Pasadena, CA 91125 USA
基金
美国国家科学基金会;
关键词
STATES;
D O I
10.1126/science.abn7293
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today's quantum processors.
引用
收藏
页码:1182 / +
页数:81
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