analiza aiqbench
This research investigates the quantization of mean return times in quantum walks under multi-channel monitoring, extending universal time statistics from one-dimensional to higher-dimensional projected subspaces via ancilla coupling. The study provides theoretical insights into how measurement strength and dimensionality influence temporal dynamics within isolated quantum systems without introducing practical AI or machine learning applications.
Quantized time in quantum walks under weak rank-K measurements
arXiv:2606.13552v1 Announce Type: new Abstract: Measurements can be used to monitor the evolution of quantum systems and may lead to a universally quantized time statistics. It is known that the mean return time is quantized for strong and indirect monitoring through the winding number of the return amplitude in a one-dimensional space. Here we discuss that under multi-channel strong or indirect monitoring, where the latter is achieved through ancilla coupling, the mean return time of a quantum walk in the projected subspace is also quantized. This reflects a universal time quantization for a higher dimensional evolution.
Abstract:Measurements can be used to monitor the evolution of quantum systems and may lead to a universally quantized time statistics. It is known that the mean return time is quantized for strong and indirect monitoring through the winding number of the return amplitude in a one-dimensional space. Here we discuss that under multi-channel strong or indirect monitoring, where the latter is achieved through ancilla coupling, the mean return time of a quantum walk in the projected subspace is also quantized. This reflects a universal time quantization for a higher dimensional evolution.
arXiv:2606.13552 [quant-ph]
(or arXiv:2606.13552v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.13552 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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.
Text integral sub licența deschisă a publicației · 182 cuvinte
Subiecte conexe
When Bits Break Recourse: Counterfactual-Faithful Quantization
arXiv · 3 aug. 2026

How to Optimize Transformer-Based Models for Low-Precision Training
NVIDIA · 16 iun. 2026
Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization
arXiv · 29 iul. 2026

Liquid AI Open-Sources Pipette: A Reproducible Benchmarking Suite That Measures On-Device Models, Quantization, Runtime and Hardware Together
MarkTechPost · 25 aug. 2026