{
"id": "2104.03295",
"version": "v1",
"published": "2021-04-07T17:53:50.000Z",
"updated": "2021-04-07T17:53:50.000Z",
"title": "Experimental Quantum Learning of a Spectral Decomposition",
"authors": [
"Michael R. Geller",
"ZoĆ« Holmes",
"Patrick J. Coles",
"Andrew Sornborger"
],
"categories": [
"quant-ph"
],
"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 4x4 unitary matrix.",
"revisions": [
{
"version": "v1",
"updated": "2021-04-07T17:53:50.000Z"
}
],
"analyses": {
"keywords": [
"experimental quantum learning",
"spectral decomposition",
"4x4 unitary matrix",
"quantum machine learning algorithms",
"small scale implementations"
],
"note": {
"typesetting": "TeX",
"pages": 0,
"language": "en",
"license": "arXiv",
"status": "editable"
}
}
}