qubithubvariational-classifier-mitarai
Faithful reproduction of the Quantum Circuit Learning framework (Mitarai et al., Phys. Rev. A 98, 032309, 2018). Demonstrates that parameterized quantum circuits trained via the parameter-shift rule are universal function approximators. Includes binary classification and sin(x) regression tasks.
- Framework
- PennyLane
- Qubits
- 4
- Depth
- 13
- Gate set
- RY, RZ, CNOT
- Licence
- Apache-2.0
- Version
- v1
- Updated
- 20 hours ago
- Runs
- None completed
README.md · 12 KB
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@software{qubithub_variationalclassifiermitarai_2026,
author = {QubitHub Circuits},
title = {Quantum Circuit Learning (Mitarai 2018)},
year = {2026},
version = {v1},
url = {https://qubithub.co/qubithub/variational-classifier-mitarai},
}Generated from this circuit's metadata. QubitHub does not mint DOIs, so this is not a registered identifier — check it against your venue's requirements before publishing.