arXiv (All)
open access
QML-PipeGuard: Detecting Channel-Substitution Attacks on Quantum Machine Learning Pipelines in Untrusted Clouds
As machine-learning workloads move onto quantum hardware they inherit a trust problem classical AI deployments have largely solved and quantum ones have not: quantum machine learning (QML) is already sold as a cloud service, and a customer cannot check that the circuit they paid for is actually the
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
- Enhancing Multi-Cloud Security with Quantum-Resilient AI for Anomaly Detection#92412
- SAFE Quantum Machine Learning with Variational Quantum Classifiers#196455
- The impact of quantum computing on cybersecurity#1178943
- Verifiable Quantum Advantage and Computation via Quantum Circuit Obfuscation#1154550
- Verifiable Quantum Advantage and Computation via Quantum Circuit Obfuscation#1136751
Record · ID 1403113
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