arXiv (All)
open access
Generative learning of time series with holographic quantum circuit sampling
Quantum generative models face growing qubit requirements and concentrated loss landscapes when learning long time series. Here we introduce holographic quantum circuit sampling (HQCS), which combines mid-circuit measurements, qubit reuse and independently parameterized temporal blocks to generate t
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
- Quantum MeanFlow: single-shot generative sampling on NISQ hardware#822580
- Hardware-Agnostic Modeling of Quantum Side-Channel Leakage via Conditional Dynamics and Learning from Full Correlation Data#1357107
- Minimizing classical resources in variational measurement-based quantum computation for generative modeling#10352
- Sampling Noise and Optimized Measurement Distribution in Imaginary-Time Quantum Dynamics Simulations#1014712
- MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning in the NISQ Era.#255275
Record · ID 1325979
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