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DOE OSTI · 1817920

Experimental quantum learning of a spectral decomposition

Geller, Michael R.·Holmes, Zoe Portia·Coles, Patrick Joseph·Sornborger, Andrew Tyler
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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 4 × 4 unitary matrix.

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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICSMachine learningquantum simulation

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Geller, Michael R., Holmes, Zoe Portia, Coles, Patrick Joseph, Sornborger, Andrew Tyler. 2021-08-30. Experimental quantum learning of a spectral decomposition. https://doi.org/10.1103/physrevresearch.3.033200

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