Measurements provide the interface between the quantum and classical worlds. In recent years it has become clear that ideas of learning theory provide profound new insights into the type of information that can be precisely acquired by means of suitable measurements. This approach is important conceptually, addressing foundational issues. It is also relevant to the application of quantum technologies: Quantum devices promise computational advantages over classical devices for specific applications. This promise can likely only be fulfilled if it makes use of appropriate quantum device read-out techniques.
Together with coauthors, Professor John Preskill has shown that quantum machines can learn from exponentially fewer experiments than the number required in conventional experiments1. He has demonstrated the difference in performance of classical and quantum machine learning models in predicting outcomes of physical experiments2. He has proven that classical machine learning algorithms can efficiently predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter3. By combining ideas of computational learning theory with methods of quantum random measurements, John Preskill has contributed significantly to providing an important new quantum-classical interface4. In addition, Professor John Preskill has provided continuing intellectual leadership to the scientific community5 in near-term quantum computing.
Professor John Preskill, Richard P. Feynman Professor of Theoretical Physics, California Institute of Technology, is most deserving of the 2024 John Stewart Bell Prize for Research on Fundamental Issues in Quantum Mechanics and Their Applications.
1 Quantum advantage in learning from experiments, H.-Y. Huang, M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskill, J. R. McClean, Science 376, 1182( 2022).
2 Information-theoretic bounds on quantum advantage in machinelearning, H.-Y. Huang, R. Kueng, J. Preskill, Phys. Rev. Lett. 126, 190505 (2021).
3 Provably efficient machine learning for quantum many-body problems, H.-Y. Huang, R. Kueng, G. Torlai, V. V. Albert, J. Preskill, Science 377, 6613 (2022).
4 Predicting many properties of a quantum system from very few measurements, H.-Y. Huang, R. Kueng, J. Preskill, Nature Physics 16, 1050 (2020).
5 Quantum computing in the NISQ era and beyond, J. Preskill, Quantum 2, 79 (2018).
The prize will be awarded at the 10th International Conference on Quantum Information and Quantum Control to be held at the Fields Institute, located at the University of Toronto, August 26 -30, 2024.