Quantum advantage in learning from experiments

By Hsin-Yuan Huang, M. Broughton, Jordan S. Cotler, Sitan Chen, Jerry Zheng Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill and 1 more. Science, 2021

690 citations recorded by the source corpus.

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. Description Learning from quantum experiments There is considerable interest in extending the recent success of quantum computers in outperforming their conventional classical counterparts (quantum advantage) from some model mathematical problems to more meaningful tasks. Huang et al. show how manipulating multiple quantum states can provide an exponential advantage over classical processing of measurements of single-quantum states for certain learning tasks. These include predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics (see the Perspective by Dunjko). In their proof-of-principle experiments using up to 40 qubits on a Google Sycamore quantum processor, the authors achieved almost four orders of magnitude of reduction in the required number of experiments over the best-known classical lower bounds. —YS Quantum-enhanced strategies can provide a dramatic performance boost in learning useful information from quantum experiments.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/57838203578fe749f27f4a12106f0190d25d1346

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Citations in the corpus

Challenges and opportunities in quantum machine learning (2022, methodology): “It was recently shown [64, 66, 115, 116] that there is an exponential quantum advantage in sample complexity when we can use a quantum sensor, quantum memory, and quantum computer to retrieve, store, and process quantum information from experiments.”
Logical quantum processor based on reconfigurable atom arrays (2023, methodology): “By making use of the full outcome distribution, we can also evaluate the absolute value of all 4 N Pauli strings, from a single dataset, in which N is the number of qubits involved in each copy of the state 22 .”