Lernen und Inferenz von Quantenergenerativen Netzwerken mit mehreren Daten

Details

Veröffentlichungs-Nr.

EP4787229

Anmeldetag

21. Januar 2026

Veröffentlichung

5. August 2026

Rechtsraum

EP

IPC

G06N3/02, G06N3/09, G06N10/60

Abstract

In an embodiment, a parameterized quantum circuit is initialized on a quantum computer for a Quantum Neural Network (QNN) with an ansatz architecture. Input data, which includes a quantum representation of a multi-dataset and an initial state of the QNN, and labels, is received. The quantum representation of the multi-datasets on the QNN is loaded and a first dataset of the multi-dataset is labelled. A label-controlled circuit associated with label-controlled input qubits of the ansatz architecture for the QNN is determined, based on the labelled first dataset. A universal circuit associated with the ansatz architecture for a prediction circuit associated with the QNN is determined, based on labelled first dataset. The prediction circuit is determined based on the label-controlled circuit and the universal circuit. The prediction circuit is trained and configured to generate predictions associated with the loaded fractionally weighted data portfolios.

Anmelder

Firma

Fujitsu Limited

Land

🇯🇵 Japan

Fachgebiete