aiqbench analysis
This paper advances the mathematical formalism of quantum optics by generalizing the concept of tags within vector spaces, offering a rigorous framework for Dirac notation that incorporates spatiotemporal and spin degrees of freedom. By deriving commutation relations and symplectic phase spaces from these properties, the work provides foundational theoretical tools rather than immediate algorithmic applications or hardware breakthroughs.
Notes an tagged vector space
arXiv:2510.17327v2 Announce Type: replace Abstract: A generalisation is provided for the notion of tags, often found in physics notation. The properties of tags and their extractors are discussed. It leads to the definition of tagged vector spaces. It is applied in the context of quantum optics, to provide a mathematical description for Dirac notation as used in physics. The commutation relations for ladder operators and a symplectic phase space are derived as consequences of the properties of tagged vector spaces. To incorporate the spatiotemporal and spin degrees of freedom, tags are indexed by functions, leading to a functional representation.
Abstract:A generalisation is provided for the notion of tags, often found in physics notation. The properties of tags and their extractors are discussed. It leads to the definition of tagged vector spaces. It is applied in the context of quantum optics, to provide a mathematical description for Dirac notation as used in physics. The commutation relations for ladder operators and a symplectic phase space are derived as consequences of the properties of tagged vector spaces. To incorporate the spatiotemporal and spin degrees of freedom, tags are indexed by functions, leading to a functional representation.
arXiv:2510.17327 [quant-ph]
(or arXiv:2510.17327v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2510.17327 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 183 words
Related coverage
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
Hugging Face · Aug 18, 2026

Exploding variance of means of exponentials: least-squares to the rescue – Machine Learning Research Blog
francisbach.com · Sep 27, 2026

Claude Code’s Next Era — Thariq Shihipar, Anthropic
Latent Space · Sep 29, 2026
How robust are natural language autoencoders to initialization?
AI Alignment Forum · Jul 10, 2026