arXiv (All)
open access
Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits
Variational quantum circuits (VQCs) face an expressivity-trainability dilemma and scalability challenges. We propose Multi-Layer Fully-Connected Variational Quantum Circuits (FC-VQC), a general-purpose quantum machine learning framework that connects local VQC blocks through measurement, determinist
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
- Learning to Program Adaptive Non-Local Observables for Machine Learning#1012827
- Learning to Program Adaptive Non-Local Observables for Machine Learning#965428
- Characterizing the loss landscape of variational quantum circuits#1158052
- Hybrid Quantum Temporal Convolutional Networks#1041455
- Quantum minds: Merging quantum computing with next-gen AI#108888
Record · ID 1129179
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