Dataset Open Access

Operator Learning for efficient Quantum Computation

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

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    <subfield code="a">&lt;p&gt;The data refers to an operator learning protocol that compiles discrete&amp;nbsp;operators into compact quantum circuits, for which a pre-print is&amp;nbsp;available via &lt;a href="https://arxiv.org/abs/2606.20184"&gt;arXiv:2606.20184&lt;/a&gt;.&amp;nbsp;The approach learns a layered sequence of local multi-qubit gates by&amp;nbsp;backpropagation combined with a unitary retraction, allows the qubit&amp;nbsp;connectivity of the target hardware to be taken into account, and&amp;nbsp;represents non-unitary operators by a block encoding with a single&amp;nbsp;ancilla qubit. The examples include propagators of the transverse-field&amp;nbsp;Ising model and of the Pariser&amp;ndash;Parr&amp;ndash;Pople model of butadiene, which are&amp;nbsp;compared with Suzuki&amp;ndash;Trotter expansions, as well as the finite-difference&amp;nbsp;approximation of the second derivative for one- and two-dimensional&amp;nbsp;qubit topologies and a dense operator arising from a panel method for&amp;nbsp;the inviscid flow around an airfoil. The repository contains one archive&amp;nbsp;per experiment (exp1.zip &amp;ndash; exp4.zip).&lt;/p&gt;</subfield>
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Publication date:
September 27, 2026
DOI:
10.25592/uhhfdm.21872

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Keyword(s):
Quantum Operator Learning Qubit Connectivity Matrix Encoding Quantum Circuits Synthesis
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Derived from:
10.48550/arXiv.2606.20184
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Version v2 10.25592/uhhfdm.21872 Sep 27, 2026
Version v1 10.25592/uhhfdm.18963 Jul 6, 2026
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