AI & ML
Sample complexity of quantum resource testing via one-shot quantum blurring
arXiv math.IT0 views
arXiv:2607.24712v3 Announce Type: replace-cross Abstract: Quantum resource testing is a fundamental primitive of quantum information processing, profoundly connected to resource manipulation. Its goal is to discriminate $n$ copies of a given resourceful state $\rho$ from all free (i.e., resourceless) states; key instances for applications are entanglement testing and quantum magic testing. The asymptotic characterisation relies on the recently proven Generalised Quantum Stein's Lemma (GQSL), which establishes the rate of decay of the false negative error probability for a fixed false positive error probability. This result, however, is intrinsically asymptotic and thus can provide no finite-resource guarantees, which makes its practical implications unclear. Here, we establish the first rigorous finite-$n$ bounds on quantum resource testing and hence quantum resource manipulation, thus strengthening the GQSL and providing explicit estimates on the number of copies needed to achieve a prescribed performance. As notable consequences, we obtain (a) the convergence of the regularised R\'enyi relative entropies of a resource, which settles the important open problem from [Fang/Hayashi, arXiv:2508.12901, IEEE ToIT 72:6, 2026]; and (b) the first sample-complexity bound for asymmetric resource testing: for any fixed false positive error probability, a false negative error probability of at most $\delta$ can be achieved with $n=O\left(\frac{\log(1/\delta)}{D^\infty(\rho\|\mathcal F)}\right)$ copies of $\rho$, in the limit where $\delta \to 0$.
Source:https://arxiv.org/abs/2607.24712PreviousTowards Diverse and Comprehensive Benchmarks for Mutual Information EstimationNextWhen Disclosure Is Compulsory: Evaluating Ride-Hailing Rebalancing Policies with Self-Interested LLM Driver Agents
Related posts
Setting a new standard for responsible AI infrastructure
0OpenAI Blog

I Stopped Writing Giant Prompts. This 3-Step Pattern Fixed My AI Output
0dev.to

GPT-Image-2.6 Flare vs Sunburst: ¿qué modelo deberías usar?
0dev.to
Writing Online for 30 Days: What Actually Brings Readers (It's Not Posting More)
0dev.to
Blind RSA in circl ignored the reader you passed it
0dev.to
HN: Envelox: Secure en Private PGP email
0Hacker News New