English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Learning about physical systems from quantum-enhanced experiments, relying ona quantum memory and quantum processing, can outperform learning fromexperiments in which only classical memory and processing are available.Whereas quantum advantages have been established for a variety of statelearning tasks, quantum process learning allows for comparable advantages onlywith a careful problem formulation and is less understood. We establish anexponential quantum advantage for learning an unknown $n$-qubit quantum process$\mathcal{N}$. We show that a quantum memory allows to efficiently solve thefollowing tasks: (a) learning the Pauli transfer matrix of an arbitrary$\mathcal{N}$, (b) predicting expectation values of bounded Pauli-sparseobservables measured on the output of an arbitrary $\mathcal{N}$ upon input ofa Pauli-sparse state, and (c) predicting expectation values of arbitrarybounded observables measured on the output of an unknown $\mathcal{N}$ withsparse Pauli transfer matrix upon input of an arbitrary state. With quantummemory, these tasks can be solved using linearly-in-$n$ many copies of the Choistate of $\mathcal{N}$, and even time-efficiently in the case of (b). Incontrast, any learner without quantum memory requires exponentially-in-$n$ manyqueries, even when querying $\mathcal{N}$ on subsystems of adaptively chosenstates and performing adaptively chosen measurements. In proving thisseparation, we extend existing shadow tomography upper and lower bounds fromstates to channels via the Choi-Jamiolkowski isomorphism. Moreover, we combinePauli transfer matrix learning with polynomial interpolation techniques todevelop a procedure for learning arbitrary Hamiltonians, which may havenon-local all-to-all interactions, from short-time dynamics. Our resultshighlight the power of quantum-enhanced experiments for learning highly complexquantum dynamics.

Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix