September 27, 2026 Dataset Open Access

Over, Paul; Bengoechea, Sergio; Borello Busilacchi, Leonardo; Kiffner, Martin; Rung, Thomas; Michailidis, Alexios A.

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  <dc:creator>Over, Paul</dc:creator>
  <dc:creator>Bengoechea, Sergio</dc:creator>
  <dc:creator>Borello Busilacchi, Leonardo</dc:creator>
  <dc:creator>Kiffner, Martin</dc:creator>
  <dc:creator>Rung, Thomas</dc:creator>
  <dc:creator>Michailidis,  Alexios A.</dc:creator>
  <dc:date>2026-09-27</dc:date>
  <dc:description>The data refers to an operator learning protocol that compiles discrete operators into compact quantum circuits, for which a pre-print is available via arXiv:2606.20184. The approach learns a layered sequence of local multi-qubit gates by backpropagation combined with a unitary retraction, allows the qubit connectivity of the target hardware to be taken into account, and represents non-unitary operators by a block encoding with a single ancilla qubit. The examples include propagators of the transverse-field Ising model and of the Pariser–Parr–Pople model of butadiene, which are compared with Suzuki–Trotter expansions, as well as the finite-difference approximation of the second derivative for one- and two-dimensional qubit topologies and a dense operator arising from a panel method for the inviscid flow around an airfoil. The repository contains one archive per experiment (exp1.zip – exp4.zip).</dc:description>
  <dc:identifier>https://www.fdr.uni-hamburg.de/record/21872</dc:identifier>
  <dc:identifier>10.25592/uhhfdm.21872</dc:identifier>
  <dc:identifier>oai:fdr.uni-hamburg.de:21872</dc:identifier>
  <dc:relation>doi:10.48550/arXiv.2606.20184</dc:relation>
  <dc:relation>doi:10.25592/uhhfdm.18962</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Quantum Operator Learning</dc:subject>
  <dc:subject>Qubit Connectivity</dc:subject>
  <dc:subject>Matrix Encoding</dc:subject>
  <dc:subject>Quantum Circuits Synthesis</dc:subject>
  <dc:title>Operator Learning for efficient Quantum Computation</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>