Diagonal Adaptive Non-local Observables on Quantum Neural Networks
The paper discusses Diagonal Adaptive Non-local Observables (ANOs) in the context of Quantum Neural Networks. It highlights how this approach can reduce the complexity and classical optimization costs associated with quantum observables. The authors propose a method that maintains the advantages of full ANOs while simplifying the computational requirements.
- ▪Diagonal ANOs can significantly enlarge the function space of Variational Quantum Algorithms.
- ▪This method reduces the complexity of $k$-local observables from $O(4^k)$ to $O(2^k).
- ▪Diagonal ANOs encompass conventional Variational Quantum Circuits as a special case.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15410 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Quantum Physics arXiv:2605.15410 (quant-ph) [Submitted on 14 May 2026] Title:Diagonal Adaptive Non-local Observables on Quantum Neural Networks Authors:Huan-Hsin Tseng, Yan Li, Hsin-Yi Lin, Samuel Yen-Chi Chen View a PDF of the paper titled Diagonal Adaptive Non-local Observables on Quantum Neural Networks, by Huan-Hsin Tseng and 3 other authors View PDF Abstract:Adaptive Non-local Observables (ANOs) have shown that making quantum observables dynamic can substantially enlarge the function space of Variational Quantum Algorithms, partly shifting hardware demands from circuit synthesis to measurement design. However, this advantage is accompanied by a steep increase in the number of parameters, as well as the classical optimization cost for varying general Hermitian observables.
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