arXiv (All)
open access
Encoding Numerical Data for Generative Quantum Machine Learning
Generative quantum machine learning models are trained to deduce the probability distribution underlying a given dataset, and to produce new, synthetic samples from it. The majority of such models proposed in the literature, like the Quantum Circuit Born Machine (QCBM), fundamentally work on a binar
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Discovering quantum phenomena with Interpretable Machine Learning#31287
- Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation#1131903
- Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization#1118599
- Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States#1049077
- Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks#151724
Record · ID 985031
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive