Quantum Advantage in Learning from Experiments | Qiskit Seminar Series

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Quantum Advantage in Learning from Experiments

Speaker: Hsin-Yuan Huang
Host: Zlatko Minev, PhD.

Abstract:

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical system to a stable quantum memory, and processes that data using a quantum computer, could have significant advantages over conventional experiments in which the physical system is measured and the outcomes are processed using a classical computer. We prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments. The exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. In some tasks, the quantum processing needed to achieve the exponential advantage can be modest; for example, one can simultaneously learn about many noncommuting observables by processing only two copies of the system. Conducting experiments with up to 40 superconducting qubits and 1300 quantum gates, we demonstrate that a substantial quantum advantage can be realized using today's relatively noisy quantum processors. Our results highlight how quantum technology can enable powerful new strategies to learn about nature.

Bio:

Hsin-Yuan Huang is a Ph.D. student at Caltech advised by John Preskill and Thomas Vidick.. His research focuses on understanding what can quantum and classical machines learn. Some of his works include a more efficient approach to learn quantum many-body system (classical shadow tomography), rigorous machine learning algorithms for solving quantum many-body problems, and provable quantum advantage in learning from experiments.

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The Qiskit Seminar Series is a deep dive into various academic and research topics within the quantum community. It will feature community members and leaders every Friday, 12 PM EDT.