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- W3198532091 abstract "Currently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine to compress the depth of dynamical quantum simulations. One can view our implementation as a demonstration of entanglement-enhanced machine learning, as only a single (entangled) training data pair is required to learn a $4ifmmodetimeselsetexttimesfi{}4$ unitary matrix." @default.
- W3198532091 created "2021-09-13" @default.
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- W3198532091 date "2021-08-30" @default.
- W3198532091 modified "2023-09-27" @default.
- W3198532091 title "Experimental quantum learning of a spectral decomposition" @default.
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- W3198532091 doi "https://doi.org/10.1103/physrevresearch.3.033200" @default.
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