Quantum advantage in learning from experiments
This work addresses the challenge of data acquisition and processing in physics experiments, offering a potential revolution in learning about nature with quantum technology, though it is incremental as it builds on existing quantum computing concepts.
The paper tackles the problem of learning from experimental data more efficiently by using quantum technology, proving that quantum machines can learn from exponentially fewer experiments than classical methods in tasks like predicting physical properties and analyzing noisy quantum states, with demonstrations on up to 40 superconducting qubits showing a substantial advantage.
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.