Dataset Open Access
Operator Learning for efficient Quantum Computation
Over, Paul;
Bengoechea, Sergio;
Borello Busilacchi, Leonardo;
Kiffner, Martin;
Rung, Thomas;
Michailidis, Alexios A.
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).
| Name | Size | |
|---|---|---|
|
exp1.zip
md5:ceff053e487208a2956a99d1fd906869 |
4.7 GB | Download |
|
exp2.zip
md5:a16df98cb57f108343eceb101cc0e426 |
79.8 MB | Download |
|
exp3.zip
md5:d9eaf1f972d8b5f2210392d4f6fd7527 |
94.9 MB | Download |
|
exp4.zip
md5:52e10a5389248904c1f4c28c3c07dc50 |
400.3 kB | Download |
|
README.md
md5:b72ddbf3b11e38fa4ea8983a7d8d1791 |
2.1 kB | Download |
- Publication date:
- September 27, 2026
- DOI:
-
- Keyword(s):
- Quantum Operator Learning Qubit Connectivity Matrix Encoding Quantum Circuits Synthesis
- Related identifiers:
- Derived from:
10.48550/arXiv.2606.20184 - Communities:
- License (for files):
- Creative Commons Attribution 4.0 International